paper_id,year,title,authors,primary_area,decision,venue,scores,avg_score,presentations,avg_presentation,soundnesses,avg_soundness,contributions,avg_contribution,confidences,avg_confidence,keywords,citations_serper,frontend_paper_id,submission_date,normalized_citations
iclr_QFO1asgas2,2025,Advantage Alignment Algorithms,"Juan Agustin Duque, Milad Aghajohari, Tim Cooijmans, razvan ciuca, Tianyu Zhang, Gauthier Gidel, Aaron Courville",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[4, 4, 3, 2]",3.25,"[4, 3, 2, 3]",3.0,"[2, 3, 2, 3]",2.5,"[4, 2, 4, 2]",3.0,"[""multi-agent reinforcement learning"", ""opponent shaping"", ""social dilemmas"", ""general-sum games""]",10,5416c597-795a-4a39-a0af-5002f75e6e69,2024-06-01,0.4471
iclr_07yvxWDSla,2025,Synthetic continued pretraining,"Zitong Yang, Neil Band, Shuangping Li, Emmanuel Candes, Tatsunori Hashimoto","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 3]",3.25,"[4, 4, 2, 4]",3.5,"[3, 3, 2, 3]",2.75,"[4, 3, 4, 3]",3.5,"[""large language model"", ""synthetic data"", ""continued pretraining""]",59,f311b515-66de-42d6-ac9d-e4e0d92444bd,2024-09-01,3.0465
iclr_YrycTjllL0,2025,BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions,"Terry Yue Zhuo, Vu Minh Chien, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, Simon Brunner, Chen GONG, James Hoang, Armel Randy Zebaze, Xiaoheng Hong, Wen-Ding Li, Jean Kaddour, Ming Xu, Zhihan Zhang, Prateek Yadav, Naman Jain, Alex Gu, Zhoujun Cheng, Jiawei Liu, Qian Liu, Zijian Wang, Binyuan Hui, Niklas Muennighoff, David Lo, Daniel Fried, Xiaoning Du, Harm de Vries, Leandro Von Werra",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 10, 10]",9.0,"[3, 4, 4, 4]",3.75,"[3, 3, 4, 4]",3.5,"[3, 4, 4, 4]",3.75,"[4, 4, 3, 4]",3.75,"[""code generation"", ""tool use"", ""instruction following"", ""benchmark""]",418,768fbe13-4781-4b62-a747-3e9ab1e6d4d8,2024-06-01,18.6885
iclr_JDm7oIcx4Y,2025,Accelerated training through iterative gradient propagation along the residual path,"Erwan Fagnou, Paul Caillon, Blaise Delattre, Alexandre Allauzen",optimization,Accept (Oral),ICLR 2025 Oral,"[6, 6, 8, 8, 8]",7.2,"[3, 3, 3, 3, 3]",3.0,"[3, 3, 3, 4, 3]",3.2,"[3, 3, 3, 3, 4]",3.2,"[4, 4, 3, 4, 4]",3.8,"[""optimization"", ""efficient training""]",3,272f9e9e-ff3c-49fe-996e-d516ae01bc79,2024-09-27,0.1622
iclr_ZCOwwRAaEl,2025,Latent Bayesian Optimization via Autoregressive Normalizing Flows,"Seunghun Lee, Jinyoung Park, Jaewon Chu, Minseo Yoon, Hyunwoo J. Kim","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 4, 3, 3]",3.5,"[3, 4, 3, 4]",3.5,"[3, 4, 4, 4]",3.75,"[5, 4, 5, 3]",4.25,"[""bayesian optimization"", ""normalizing flow""]",9,d64953f7-f197-4646-a523-fef9aca0d83e,2024-09-27,0.4865
iclr_Q6a9W6kzv5,2025,PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding,"Wei Chow, Jiageng Mao, Boyi Li, Daniel Seita, Vitor Campagnolo Guizilini, Yue Wang",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 2, 3, 2]",2.75,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 4]",3.25,"[4, 4, 3, 4]",3.75,"[""vision-language"", ""multi-modal understanding""]",84,291d1d70-945d-486a-8a8f-6b935a10961a,2024-09-13,4.4288
iclr_OfjIlbelrT,2025,FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference,"Xunhao Lai, Jianqiao Lu, Yao Luo, Yiyuan Ma, Xun Zhou",generative models,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 4, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 3]",2.75,"[4, 3, 4, 3]",3.5,"[""large language models (llms)"", ""llm inference"", ""long-context llms"", ""sparse attention mechanism""]",72,6972c645-02f6-457a-991c-01b532b28ac0,2024-09-16,3.8163
iclr_6EUtjXAvmj,2025,Variational Diffusion Posterior Sampling with Midpoint Guidance,"Badr MOUFAD, Yazid Janati, Lisa Bedin, Alain Oliviero Durmus, randal douc, Eric Moulines, Jimmy Olsson","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 3]",3.0,"[3, 4, 4, 4]",3.75,"[3, 3, 3, 4]",3.25,"[3, 5, 4, 4]",4.0,"[""diffusion models"", ""inverse problems"", ""posterior sampling""]",24,d45c06a0-c782-44fb-874f-4d4aa51a724d,2024-09-23,1.288
iclr_r5IXBlTCGc,2025,Consistency Checks for Language Model Forecasters,"Daniel Paleka, Abhimanyu Pallavi Sudhir, Alejandro Alvarez, Vineeth Bhat, Adam Shen, Evan Wang, Florian Tramèr",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 5, 8]",7.25,"[4, 3, 2, 4]",3.25,"[3, 4, 2, 4]",3.25,"[3, 3, 3, 4]",3.25,"[2, 3, 4, 4]",3.25,"[""forecasting"", ""markets"", ""trading"", ""llm"", ""evaluation"", ""eval"", ""consistency"", ""robustness""]",9,90dfb362-dd31-474d-9dc4-48fd0a83177c,2024-09-28,0.4874
iclr_xByvdb3DCm,2025,When Selection Meets Intervention: Additional Complexities in Causal Discovery,"Haoyue Dai, Ignavier Ng, Jianle Sun, Zeyu Tang, Gongxu Luo, Xinshuai Dong, Peter Spirtes, Kun Zhang",causal reasoning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8, 8]",8.0,"[3, 3, 3, 3, 2]",2.8,"[3, 3, 3, 3, 3]",3.0,"[4, 3, 3, 3, 3]",3.2,"[3, 3, 3, 2, 3]",2.8,"[""causal discovery"", ""selection bias"", ""experiments"", ""interventions""]",7,40845d5a-f565-4b4d-a898-a118c2e1703f,2024-09-17,0.3717
iclr_kRoWeLTpL4,2025,Copyright-Protected Language Generation via Adaptive Model Fusion,"Javier Abad, Konstantin Donhauser, Francesco Pinto, Fanny Yang","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[6, 8, 8, 8]",7.5,"[3, 4, 4, 4]",3.75,"[3, 4, 3, 3]",3.25,"[2, 3, 3, 3]",2.75,"[3, 3, 3, 3]",3.0,"[""language models"", ""copyright"", ""model fusion"", ""memorization"", ""safety"", ""privacy""]",10,ef20321b-707f-4146-a813-5d482729edfb,2024-09-26,0.5396
iclr_UV5p3JZMjC,2025,Learning Randomized Algorithms with Transformers,"Johannes Von Oswald, Seijin Kobayashi, Yassir Akram, Angelika Steger","other topics in machine learning (i.e., none of the above)",Accept (Oral),ICLR 2025 Oral,"[8, 6, 6, 6]",6.5,"[3, 2, 4, 4]",3.25,"[3, 3, 3, 3]",3.0,"[3, 2, 3, 3]",2.75,"[4, 4, 3, 3]",3.5,"[""randomized algorithms"", ""learning under adversarial losses"", ""adversarial robustness"", ""in-context learning algorithms""]",4,2bce222e-0c48-4667-9973-ac6433cff874,2024-08-01,0.1964
iclr_JWtrk7mprJ,2025,Residual Deep Gaussian Processes on Manifolds,"Kacper Wyrwal, Andreas Krause, Viacheslav Borovitskiy","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8, 8]",7.6,"[4, 4, 3, 4, 3]",3.6,"[3, 4, 3, 3, 4]",3.4,"[3, 3, 2, 3, 4]",3.0,"[3, 2, 3, 4, 4]",3.2,"[""gaussian processes"", ""manifolds"", ""deep gaussian processes"", ""probabilistic methods"", ""variational inference"", ""uncertainty quantification"", ""geometric learning""]",3,3a6b288a-dcbc-464c-882c-81a380d7cd96,2024-09-27,0.1622
iclr_dhAL5fy8wS,2025,Data Selection via Optimal Control for Language Models,"Yuxian Gu, Li Dong, Hongning Wang, Yaru Hao, Qingxiu Dong, Furu Wei, Minlie Huang","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 6, 10]",8.0,"[4, 4, 3, 3, 4]",3.6,"[3, 3, 3, 3, 3]",3.0,"[3, 3, 4, 3, 3]",3.2,"[2, 3, 3, 3, 2]",2.6,"[""pre-training language models"", ""data selection"", ""optimal control""]",31,33ca5fde-90fc-4497-b158-ee26d87d467b,2024-09-20,1.6548
iclr_1pXzC30ry5,2025,RMP-SAM: Towards Real-Time Multi-Purpose Segment Anything,"Shilin Xu, Haobo Yuan, Qingyu Shi, Lu Qi, Jingbo Wang, Yibo Yang, Yining Li, Kai Chen, Yunhai Tong, Bernard Ghanem, Xiangtai Li, Ming-Hsuan Yang","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[3, 3, 2, 2]",2.5,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 3]",2.75,"[3, 4, 4, 3]",3.5,"[""segment anything; real-time segmentation; multi-purpose model;""]",8,2800dfe0-36c8-4e1c-affa-7c3775ccddd6,2024-01-01,0.2923
iclr_KSLkFYHlYg,2025,"ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design","Keir Adams, Kento Abeywardane, Jenna Fromer, Connor W. Coley","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 10, 6]",8.0,"[4, 4, 3]",3.67,"[2, 4, 3]",3.0,"[4, 4, 3]",3.67,"[5, 5, 4]",4.67,"[""3d molecular generation"", ""drug design"", ""molecules""]",14,268eac77-2abb-4115-bb41-01c338012806,2024-09-27,0.7568
iclr_nwDRD4AMoN,2025,Artificial Kuramoto Oscillatory Neurons,"Takeru Miyato, Sindy Löwe, Andreas Geiger, Max Welling","unsupervised, self-supervised, semi-supervised, and supervised representation learning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 10, 10]",9.0,"[3, 2, 3, 4]",3.0,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 4]",3.25,"[4, 3, 4, 4]",3.75,"[""oscillatory neurons"", ""feature binding"", ""object-centric learning"", ""reasoning"", ""adversarial robustness""]",37,708184d9-f8b5-4eb4-a10e-a1d82ef4cee4,2024-09-15,1.9577
iclr_gc8QAQfXv6,2025,Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning,"Gangwei Jiang, Caigao JIANG, Zhaoyi Li, Siqiao Xue, JUN ZHOU, Linqi Song, Defu Lian, Ying Wei","transfer learning, meta learning, and lifelong learning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 10, 10]",9.0,"[3, 2, 2, 4]",2.75,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 4]",3.25,"[3, 4, 3, 4]",3.5,"[""catastrophic forgetting; large language model; instruction tuning""]",16,b34d32b8-3b1e-4917-8ea8-0d6979543b64,2024-09-25,0.8618
iclr_g3xuCtrG6H,2025,A Computational Framework for Modeling Emergence of Color Vision in the Human Brain,"Atsunobu Kotani, Ren Ng",applications to neuroscience & cognitive science,Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 10, 8]",8.0,"[3, 3, 4, 3, 3]",3.2,"[3, 3, 4, 4, 2]",3.2,"[2, 3, 3, 4, 2]",2.8,"[3, 4, 5, 5, 3]",4.0,"[""color vision"", ""computational neuroscience"", ""retina simulation"", ""cortical learning"", ""self-supervised learning"", ""color blindness""]",0,5c1fc6a1-8f4f-4a86-8cc3-5a78a61ef583,2024-08-01,0.0
iclr_Iyrtb9EJBp,2025,Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse,"Maojia Song, Shang Hong Sim, Rishabh Bhardwaj, Hai Leong Chieu, Navonil Majumder, Soujanya Poria","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 3]",3.25,"[4, 3, 3, 3]",3.25,"[4, 4, 3, 3]",3.5,"[4, 2, 3, 3]",3.0,"[""large language models"", ""trustworthiness"", ""hallucinations"", ""retrieval augmented generation""]",31,d6ed0601-a224-4de7-8245-9035750d224a,2024-09-01,1.6007
iclr_OvoCm1gGhN,2025,Differential Transformer,"Tianzhu Ye, Li Dong, Yuqing Xia, Yutao Sun, Yi Zhu, Gao Huang, Furu Wei","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 4, 3, 3]",3.5,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[4, 4, 4, 4]",4.0,"[""sequence modeling"", ""language models"", ""model architecture"", ""transformer""]",285,b1282863-c1ed-45a1-9230-743c412d3a6e,2024-09-22,15.2679
iclr_CRmiX0v16e,2025,Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation,"Mohamed El Amine Boudjoghra, Angela Dai, Jean Lahoud, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Fahad Shahbaz Khan","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 5, 8, 8, 10]",7.8,"[3, 3, 4, 3, 4]",3.4,"[3, 2, 4, 3, 3]",3.0,"[3, 2, 3, 3, 3]",2.8,"[3, 4, 3, 4, 5]",3.8,"[""open vocabulary"", ""3d point cloud instance segmentation""]",29,b6aae41c-dc85-468e-8ca0-d83a080834d7,2024-06-01,1.2966
iclr_4OaO3GjP7k,2025,Flat Reward in Policy Parameter Space Implies Robust Reinforcement Learning,"Hyun Kyu Lee, Sung Whan Yoon",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[2, 2, 2, 3]",2.25,"[3, 3, 2, 3]",2.75,"[2, 3, 2, 3]",2.5,"[3, 3, 3, 3]",3.0,"[""reinforcement learning"", ""flat minima"", ""robust reinforcement learning""]",7,2fdd7681-da3e-4b5e-bdbd-4a727fc774a1,2024-09-21,0.3743
iclr_VVixJ9QavY,2025,Reasoning Elicitation in Language Models via Counterfactual Feedback,"Alihan Hüyük, Xinnuo Xu, Jacqueline R. M. A. Maasch, Aditya V. Nori, Javier Gonzalez",causal reasoning,Accept (Oral),ICLR 2025 Oral,"[6, 5, 8, 6]",6.25,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 2]",3.0,"[3, 2, 3, 2]",2.5,"[3, 4, 3, 4]",3.5,"[""language models"", ""reasoning"", ""fine-tuning"", ""counterfactuals""]",11,0327a026-3de0-4bc0-840b-4b9c9f8a30f8,2024-09-27,0.5946
iclr_cH65nS5sOz,2025,Subgraph Federated Learning for Local Generalization,"Sungwon Kim, Yoonho Lee, Yunhak Oh, Namkyeong Lee, Sukwon Yun, Junseok Lee, Sein Kim, Carl Yang, Chanyoung Park",learning on graphs and other geometries & topologies,Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 6, 10]",7.6,"[4, 3, 3, 3, 4]",3.4,"[3, 3, 3, 3, 4]",3.2,"[3, 2, 3, 3, 4]",3.0,"[3, 4, 3, 3, 4]",3.4,"[""graph neural networks"", ""graph federated learning""]",9,5a74027d-81bd-4032-8c76-960125e90134,2024-09-26,0.4856
iclr_Fur0DtynPX,2025,GridMix: Exploring Spatial Modulation for Neural Fields in PDE Modeling,"Honghui Wang, Shiji Song, Gao Huang","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 4, 3, 3]",3.25,"[3, 4, 3, 3]",3.25,"[3, 3, 3, 2]",2.75,"[2, 3, 4, 4]",3.25,"[""partial differential equations"", ""neural fields""]",7,cd2df161-9323-4f5c-b6d0-f707f45ef65e,2024-09-20,0.3737
iclr_z5uVAKwmjf,2025,AFlow: Automating Agentic Workflow Generation,"Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xiong-Hui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, Bingnan Zheng, Bang Liu, Yuyu Luo, Chenglin Wu","applications to robotics, autonomy, planning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[3, 3, 1, 3]",2.5,"[3, 3, 3, 3]",3.0,"[4, 4, 3, 3]",3.5,"[3, 3, 3, 3]",3.0,"[""llm agent; prompt optimization; workflow generation""]",325,871ff9c9-0d39-44f8-bc48-99ff7fd2346e,2024-09-17,17.2566
iclr_Wr3UuEx72f,2025,LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior,"Hanyu Wang, Saksham Suri, Yixuan Ren, Hao Chen, Abhinav Shrivastava",generative models,Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[3, 3, 3, 2]",2.75,"[2, 3, 3, 2]",2.5,"[3, 3, 3, 3]",3.0,"[4, 5, 4, 3]",4.0,"[""video generation"", ""visual tokenization""]",37,7cd114fe-4e68-48bb-be56-bb418520e624,2024-09-26,1.9964
iclr_trKNi4IUiP,2025,Robustness Inspired Graph Backdoor Defense,"Zhiwei Zhang, Minhua Lin, Junjie Xu, Zongyu Wu, Enyan Dai, Suhang Wang","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[3, 3, 3, 3]",3.0,"[3, 2, 2, 3]",2.5,"[3, 3, 2, 3]",2.75,"[2, 4, 3, 3]",3.0,"[""backdoor defense"", ""graph neural network""]",9,39ec76fb-fefa-481f-a827-8852f8aba2d1,2024-06-01,0.4024
iclr_odjMSBSWRt,2025,DarkBench: Benchmarking Dark Patterns in Large Language Models,"Esben Kran, Hieu Minh Nguyen, Akash Kundu, Sami Jawhar, Jinsuk Park, Mateusz Maria Jurewicz","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[6, 8, 6, 8]",7.0,"[2, 3, 2, 2]",2.25,"[3, 2, 3, 2]",2.5,"[2, 3, 3, 3]",2.75,"[4, 4, 4, 4]",4.0,"[""dark patterns"", ""ai deception"", ""large language models""]",34,14718378-b1ac-45fd-8418-e4e61d177dd4,2024-09-28,1.8412
iclr_KIgaAqEFHW,2025,miniCTX: Neural Theorem Proving with (Long-)Contexts,"Jiewen Hu, Thomas Zhu, Sean Welleck",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[4, 3, 3]",3.33,"[3, 3, 4]",3.33,"[4, 3, 4]",3.67,"[3, 4, 4]",3.67,"[""neural theorem proving"", ""formal mathematics"", ""benchmark dataset""]",18,1053f820-cee5-457d-a3f7-b6890eb4b133,2024-08-01,0.8838
iclr_OIvg3MqWX2,2025,"A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules","Shih-Hsin Wang, Yuhao Huang, Justin M. Baker, Yuan-En Sun, Qi Tang, Bao Wang",learning on graphs and other geometries & topologies,Accept (Oral),ICLR 2025 Oral,"[8, 10, 6, 8]",8.0,"[3, 3, 3, 3]",3.0,"[3, 4, 3, 3]",3.25,"[3, 4, 2, 3]",3.0,"[3, 5, 4, 5]",4.25,"[""graph representation"", ""sparsity"", ""connectivity"", ""rigidity"", ""molecules"", ""learning""]",8,315c2dfb-f74b-4147-b73b-9fa729fa2596,2024-09-26,0.4317
iclr_TwJrTz9cRS,2025,HiRA: Parameter-Efficient Hadamard High-Rank Adaptation for Large Language Models,"Qiushi Huang, Tom Ko, Zhan Zhuang, Lilian Tang, Yu Zhang","other topics in machine learning (i.e., none of the above)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 3, 3]",3.0,"[3, 3, 4]",3.33,"[3, 3, 3]",3.0,"[4, 4, 4]",4.0,"[""parametric-efficient fine-tuning"", ""large language model""]",50,5f6f0ef2-e53f-45e2-ab26-181ea539211e,2024-09-26,2.6978
iclr_FVuqJt3c4L,2025,Population Transformer: Learning Population-level Representations of Neural Activity,"Geeling Chau, Christopher Wang, Sabera J Talukder, Vighnesh Subramaniam, Saraswati Soedarmadji, Yisong Yue, Boris Katz, Andrei Barbu",applications to neuroscience & cognitive science,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 5, 8, 8]",7.5,"[3, 3, 3, 1, 2, 2]",2.33,"[3, 3, 3, 3, 3, 3]",3.0,"[3, 3, 3, 3, 4, 3]",3.17,"[3, 2, 3, 2, 3, 5]",3.0,"[""representation learning"", ""neuroscience"", ""self supervised learning""]",25,2c95eae8-5b7d-46a2-aa45-a7f273f31d75,2024-09-26,1.3489
iclr_bnINPG5A32,2025,RB-Modulation: Training-Free Stylization using Reference-Based Modulation,"Litu Rout, Yujia Chen, Nataniel Ruiz, Abhishek Kumar, Constantine Caramanis, Sanjay Shakkottai, Wen-Sheng Chu",generative models,Accept (Oral),ICLR 2025 Oral,"[10, 8, 6, 8]",8.0,"[3, 3, 3, 3]",3.0,"[3, 4, 3, 4]",3.5,"[3, 4, 3, 3]",3.25,"[5, 4, 4, 4]",4.25,"[""inverse problems"", ""generative modeling"", ""diffusion models"", ""posterior sampling"", ""optimal control"", ""test-time optimization""]",24,1f213fac-1752-4432-9d12-d3a5bb050453,2024-09-27,1.2973
iclr_6Mxhg9PtDE,2025,Safety Alignment Should be Made More Than Just a Few Tokens Deep,"Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Prateek Mittal, Peter Henderson","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[10, 10, 8, 10]",9.5,"[4, 3, 2, 4]",3.25,"[4, 3, 3, 3]",3.25,"[3, 4, 2, 3]",3.0,"[4, 2, 5, 5]",4.0,"[""safety alignment"", ""ai safety"", ""llm""]",339,4134bbd2-548a-4aee-b59e-be7049f90e1a,2024-06-01,15.1565
iclr_9pW2J49flQ,2025,DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL,"Mathias Jackermeier, Alessandro Abate",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 2, 3, 4]",3.0,"[5, 5, 4, 4]",4.5,"[""reinforcement learning"", ""linear temporal logic"", ""ltl"", ""generalization""]",28,f00d99cc-7e34-44f4-ae19-14d0a9c56ebb,2024-09-24,1.5054
iclr_k3tbMMW8rH,2025,Feedback Schrödinger Bridge Matching,"Panagiotis Theodoropoulos, Nikolaos Komianos, Vincent Pacelli, Guan-Horng Liu, Evangelos Theodorou",generative models,Accept (Oral),ICLR 2025 Oral,"[6, 6, 8, 8]",7.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 2]",2.75,"[3, 3, 3, 3]",3.0,"[1, 4, 5, 5]",3.75,"[""diffusion models"", ""schr\u00f6dinger bridge"", ""distribution matching"", ""semi-supervised learning""]",7,3a6b5342-2c02-4200-a0b6-63efc9a55a55,2024-09-26,0.3777
iclr_stUKwWBuBm,2025,Tractable Multi-Agent Reinforcement Learning through Behavioral Economics,"Eric Mazumdar, Kishan Panaganti, Laixi Shi",learning theory,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[2, 4, 4, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 2]",2.75,"[4, 4, 3, 3]",3.5,"[""behavioral economics"", ""risk-aversion"", ""multi-agent reinforcement learning"", ""quantal response"", ""bounded rationality""]",8,1ce67636-04b2-48c7-9c43-f0e33d7361ff,2024-09-25,0.4309
iclr_st77ShxP1K,2025,"Do as We Do, Not as You Think: the Conformity of Large Language Models","Zhiyuan Weng, Guikun Chen, Wenguan Wang","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 6]",7.5,"[4, 3, 3, 4]",3.5,"[4, 4, 3, 3]",3.5,"[3, 3, 3, 3]",3.0,"[4, 3, 3, 3]",3.25,"[""large language models"", ""conformity"", ""multi-agent system""]",33,9274a99b-05b3-4c82-a104-9521c3a12798,2024-09-15,1.746
iclr_8zJRon6k5v,2025,Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series,"Byoungwoo Park, Hyungi Lee, Juho Lee","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 2, 2, 3]",2.75,"[4, 4, 3, 3]",3.5,"[3, 4, 3, 3]",3.25,"[2, 2, 2, 3]",2.25,"[""stochastic optimal control"", ""variational inference"", ""state space model"", ""irregular time series""]",6,6a6a4535-339a-424f-aca5-2fb7ec23fe23,2024-09-26,0.3237
iclr_FpiCLJrSW8,2025,"More RLHF, More Trust? On The Impact of Preference Alignment On Trustworthiness","Aaron Jiaxun Li, Satyapriya Krishna, Himabindu Lakkaraju","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 6]",7.0,"[4, 4, 3, 4]",3.75,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 4, 3, 3]",3.25,"[""large language model"", ""trustworthy ml"", ""data attribution""]",12,2b437cd7-9497-4ebb-b5a1-df5958296027,2024-04-01,0.4925
iclr_sbG8qhMjkZ,2025,Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent,"Sayan Banerjee, Krishna Balasubramanian, PROMIT GHOSAL",learning theory,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 2, 4]",3.0,"[4, 4, 3, 4]",3.75,"[4, 4, 3, 4]",3.75,"[3, 5, 3, 4]",3.75,"[""stein variational gradient descent"", ""non-asymptotic rates"", ""variational inference""]",14,8e520cba-cf9d-4428-8c0a-0ec27d2668a0,2024-09-01,0.7229
iclr_o5TsWTUSeF,2025,ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding,"Zhengzhuo Xu, Bowen Qu, Yiyan Qi, SiNan Du, Chengjin Xu, Chun Yuan, Jian Guo","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[6, 5, 8, 8]",6.75,"[4, 3, 3, 3]",3.25,"[4, 2, 4, 3]",3.25,"[3, 3, 3, 3]",3.0,"[2, 4, 4, 3]",3.25,"[""multimodal large language models"", ""chart reasoning"", ""mixture of expert""]",51,58dabe2c-df6e-4f2f-aa00-8b70fac16456,2024-09-01,2.6334
iclr_HsHxSN23rM,2025,STAR: Synthesis of Tailored Architectures,"Armin W Thomas, Rom Parnichkun, Alexander Amini, Stefano Massaroli, Michael Poli","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[6, 8, 8, 6]",7.0,"[3, 4, 4, 3]",3.5,"[3, 3, 3, 2]",2.75,"[2, 3, 3, 2]",2.5,"[3, 3, 4, 4]",3.5,"[""alternative architectures"", ""deep signal processing"", ""language models""]",9,c89850c3-1a05-4ff1-b455-277a62790efb,2024-09-27,0.4865
iclr_xoXn62FzD0,2025,Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo,"João Loula, Benjamin LeBrun, Li Du, Ben Lipkin, Clemente Pasti, Gabriel Grand, Tianyu Liu, Yahya Emara, Marjorie Freedman, Jason Eisner, Ryan Cotterell, Vikash Mansinghka, Alexander K. Lew, Tim Vieira, Timothy J. O'Donnell","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 4, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[5, 3, 4, 3]",3.75,"[""sequential monte carlo"", ""language models"", ""semantic parsing"", ""bayesian inference"", ""probabilistic programming"", ""smc""]",48,4388de0f-0be8-4e91-b13c-df42f940925e,2024-09-27,2.5946
iclr_gHLWTzKiZV,2025,Composing Unbalanced Flows for Flexible Docking and Relaxation,"Gabriele Corso, Vignesh Ram Somnath, Noah Getz, Regina Barzilay, Tommi Jaakkola, Andreas Krause","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 8, 10]",8.0,"[3, 3, 3, 3, 3]",3.0,"[3, 3, 3, 3, 3]",3.0,"[3, 3, 3, 3, 3]",3.0,"[3, 3, 3, 2, 5]",3.2,"[""molecular docking"", ""flow matching"", ""structure relaxation"", ""unbalanced transport""]",13,d19f20ec-18b1-4f3a-a3c1-701a701574c3,2024-09-24,0.6989
iclr_uAFHCZRmXk,2025,"Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models","Simon Schrodi, David T. Hoffmann, Max Argus, Volker Fischer, Thomas Brox","unsupervised, self-supervised, semi-supervised, and supervised representation learning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 2]",2.75,"[3, 4, 3, 3]",3.25,"[3, 3, 4, 3]",3.25,"[3, 4, 3, 4]",3.5,"[""clip"", ""modality gap"", ""object bias"", ""contrastive loss"", ""data-centric"", ""vision language models"", ""vlm""]",17,219d167f-bb20-4fbc-9b7e-7ba5299f26f4,2024-04-01,0.6977
iclr_tPNHOoZFl9,2025,Learning Dynamics of LLM Finetuning,"Yi Ren, Danica J. Sutherland","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[10, 8, 8, 6]",8.0,"[4, 4, 4, 3]",3.75,"[4, 4, 4, 3]",3.75,"[4, 4, 3, 2]",3.25,"[4, 3, 3, 3]",3.25,"[""learning dynamics"", ""llm"", ""finetuning"", ""dpo""]",119,08c43c56-9029-4b27-aa69-79d3edcc8ebb,2024-07-01,5.5694
iclr_Ozo7qJ5vZi,2025,KAN: Kolmogorov–Arnold Networks,"Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 6, 8]",7.2,"[3, 4, 3, 2, 3]",3.0,"[2, 3, 2, 3, 3]",2.6,"[3, 4, 3, 3, 3]",3.2,"[4, 3, 4, 4, 3]",3.6,"[""kolmogorov-arnold networks"", ""kolmogorov-arnold representation theorem"", ""learnable activation functions"", ""interpretability"", ""ai + science""]",3851,13edda06-b05b-48a3-b19a-fffddbd6bd52,2024-04-01,158.0438
iclr_WJaUkwci9o,2025,Self-Improvement in Language Models: The Sharpening Mechanism,"Audrey Huang, Adam Block, Dylan J Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, Akshay Krishnamurthy",learning theory,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 4, 3, 3]",3.5,"[3, 3, 3, 3]",3.0,"[4, 3, 3, 3]",3.25,"[3, 3, 2, 3]",2.75,"[""learning theory"", ""sample complexity"", ""self-improvement"", ""language models""]",71,660de9aa-13e6-4047-8cd7-9aa347fe4032,2024-09-27,3.8378
iclr_gQlxd3Mtru,2025,Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport,"Zhenyi Zhang, Tiejun Li, Peijie Zhou","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[10, 8, 8]",8.67,"[3, 3, 2]",2.67,"[3, 3, 3]",3.0,"[4, 3, 2]",3.0,"[2, 3, 5]",3.33,"[""optimal transport"", ""schr\u00f6dinger bridge"", ""trajectory inference"", ""single-cell""]",35,b191f6ab-6390-4588-b7ce-54a5e6cf6f19,2024-09-24,1.8817
iclr_aWXnKanInf,2025,TopoLM: brain-like spatio-functional organization in a topographic language model,"Neil Rathi, Johannes Mehrer, Badr AlKhamissi, Taha Osama A Binhuraib, Nicholas Blauch, Martin Schrimpf",applications to neuroscience & cognitive science,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[4, 3, 3]",3.33,"[3, 4, 3]",3.33,"[2, 4, 3]",3.0,"[3, 4, 4]",3.67,"[""language modeling"", ""topography"", ""fmri"", ""neuroscience""]",12,03fdbb9a-c578-4b4e-9c8f-3a52c7a2cf30,2024-09-28,0.6498
iclr_8enWnd6Gp3,2025,TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes,"Minghao Guo, Bohan Wang, Kaiming He, Wojciech Matusik","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[6, 8, 8, 8, 8]",7.6,"[3, 4, 3, 3, 3]",3.2,"[3, 3, 3, 4, 3]",3.2,"[2, 3, 3, 3, 3]",2.8,"[5, 2, 2, 4, 4]",3.4,"[""geometry representation"", ""3d modeling""]",24,3c6b6a60-142f-4bb2-8d74-0627dbf21652,2024-05-01,1.0271
iclr_25kAzqzTrz,2025,Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning,"Jingyang Li, Jiachun Pan, Vincent Y. F. Tan, Kim-chuan Toh, Pan Zhou","unsupervised, self-supervised, semi-supervised, and supervised representation learning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 4, 3]",3.25,"[3, 3, 3, 3]",3.0,"[4, 4, 3, 4]",3.75,"[4, 3, 3, 4]",3.5,"[""deep semi-supervised learning"", ""generalization error"", ""feature learning""]",13,0d423051-ee1a-490c-a6b7-cc96ff71b606,2024-09-23,0.6977
iclr_wg1PCg3CUP,2025,Scaling Laws for Precision,"Tanishq Kumar, Zachary Ankner, Benjamin Frederick Spector, Blake Bordelon, Niklas Muennighoff, Mansheej Paul, Cengiz Pehlevan, Christopher Re, Aditi Raghunathan","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 4]",3.25,"[""quantization"", ""scaling laws"", ""precision"", ""language models""]",101,232bfea1-071d-47e7-a05a-5d5ee1254bd4,2024-09-27,5.4595
iclr_4xWQS2z77v,2025,Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality,"Sungyoon Kim, Aaron Mishkin, Mert Pilanci",learning theory,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8, 8]",8.0,"[4, 3, 3, 2, 3]",3.0,"[3, 3, 3, 3, 3]",3.0,"[3, 3, 3, 3, 3]",3.0,"[5, 2, 3, 3, 4]",3.4,"[""convex duality"", ""machine learning theory"", ""loss landscape"", ""optimal sets""]",8,983e3c90-c8f1-4a2e-83fa-0304b94816a6,2024-09-27,0.4324
iclr_V4K9h1qNxE,2025,Attention as a Hypernetwork,"Simon Schug, Seijin Kobayashi, Yassir Akram, Joao Sacramento, Razvan Pascanu",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8, 8]",7.6,"[3, 3, 3, 2, 3]",2.8,"[3, 4, 3, 3, 3]",3.2,"[3, 3, 3, 2, 2]",2.6,"[3, 4, 2, 2, 4]",3.0,"[""attention"", ""compositional generalization"", ""abstract reasoning"", ""in-context learning"", ""transformer"", ""mechanistic interpretability""]",23,00c07e01-c785-43dc-b4e2-1e91693c61f4,2024-06-01,1.0283
iclr_fV0t65OBUu,2025,Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching,"Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber",generative models,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 2, 4, 4]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 2]",2.75,"[4, 3, 3, 3]",3.25,"[""diffusion model"", ""generative model"", ""probalistic modelling""]",10,54dc09f5-1ab2-40c2-a285-77e33bddcd4e,2024-06-01,0.4471
iclr_uHLgDEgiS5,2025,Capturing the Temporal Dependence of Training Data Influence,"Jiachen T. Wang, Dawn Song, James Zou, Prateek Mittal, Ruoxi Jia",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 3]",2.75,"[""data attribution""]",26,88136b43-76f4-4f1e-a6b2-f369cf55dab0,2024-09-27,1.4054
iclr_uKZdlihDDn,2025,Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks,"Mario Lino Valencia, Tobias Pfaff, Nils Thuerey","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 6, 8]",7.6,"[2, 3, 4, 2, 4]",3.0,"[4, 3, 3, 3, 4]",3.4,"[3, 3, 4, 2, 3]",3.0,"[3, 5, 3, 2, 2]",3.0,"[""graph neural networks"", ""diffusion models"", ""physics simulations""]",26,70cfce83-f134-433b-942e-1cf228109ce7,2024-09-24,1.3978
iclr_FSjIrOm1vz,2025,Inference Scaling for Long-Context Retrieval Augmented Generation,"Zhenrui Yue, Honglei Zhuang, Aijun Bai, Kai Hui, Rolf Jagerman, Hansi Zeng, Zhen Qin, Dong Wang, Xuanhui Wang, Michael Bendersky","other topics in machine learning (i.e., none of the above)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 4]",3.5,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 4]",3.0,"[""inference scaling"", ""long-context llm"", ""retrieval augmented generation""]",99,122b9cd2-951a-44a6-8cf3-b9cc94ffa5f5,2024-09-27,5.3514
iclr_LbEWwJOufy,2025,TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation,"Haiyang Liu, Xingchao Yang, Tomoya Akiyama, Yuantian Huang, Qiaoge Li, Shigeru Kuriyama, Takafumi Taketomi","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[10, 8, 8, 8]",8.5,"[4, 2, 4, 4]",3.5,"[4, 3, 4, 4]",3.75,"[4, 3, 4, 4]",3.75,"[5, 4, 5, 5]",4.75,"[""co-speech video generation"", ""cross-modal retrieval"", ""audio repsentation learning"", ""motion repsentation learning"", ""video frame interpolation""]",31,8dc0bafd-62bf-4d5b-b849-4cac91d9e584,2024-09-26,1.6727
iclr_Mfnh1Sqdwf,2025,Learning to Discover Regulatory Elements for Gene Expression Prediction,"Xingyu Su, Haiyang Yu, Degui Zhi, Shuiwang Ji","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 6]",7.5,"[3, 3, 3, 2]",2.75,"[3, 4, 4, 3]",3.5,"[3, 3, 3, 3]",3.0,"[4, 4, 3, 3]",3.5,"[""gene expression"", ""deep learning"", ""sequence modeling""]",7,8af73253-bf0e-4b40-8379-4a4cb0218093,2024-09-28,0.3791
iclr_OlzB6LnXcS,2025,One Step Diffusion via Shortcut Models,"Kevin Frans, Danijar Hafner, Sergey Levine, Pieter Abbeel",generative models,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 4, 3, 3]",3.25,"[3, 4, 3, 4]",3.5,"[4, 3, 3, 3]",3.25,"[4, 4, 4, 4]",4.0,"[""diffusion"", ""flow-matching"", ""fast inference"", ""distillation""]",236,20924433-4fa8-43f8-8864-9e868b326e34,2024-09-25,12.711
iclr_fMTPkDEhLQ,2025,Tight Lower Bounds under Asymmetric High-Order Hölder Smoothness and Uniform Convexity,"Site Bai, Brian Bullins",optimization,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 4, 4, 3]",3.5,"[3, 4, 3, 3]",3.25,"[4, 4, 2, 3]",3.25,"[4, 3, 4, 3]",3.5,"[""convex optimization"", ""uniform convexity"", ""lower bound"", ""high-order method"", ""regularization"", ""h\u00f6lder smoothness""]",2,4bf38a6a-630a-4d8b-a454-c7344e2c057b,2024-09-01,0.1033
iclr_5Jc7r5aqHJ,2025,Energy-based Backdoor Defense Against Federated Graph Learning,"Guancheng Wan, Zitong Shi, Wenke Huang, Guibin Zhang, Dacheng Tao, Mang Ye","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[6, 8, 8, 8]",7.5,"[2, 3, 3, 3]",2.75,"[3, 3, 3, 3]",3.0,"[3, 3, 4, 3]",3.25,"[4, 5, 4, 5]",4.5,"[""federated learning"", ""graph learning""]",20,ca39d622-313d-4f2e-85f3-61f50f0fda9f,2024-09-27,1.0811
iclr_EjJGND0m1x,2025,MIND over Body: Adaptive Thinking using Dynamic Computation,"Mrinal Mathur, Barak A. Pearlmutter, Sergey M. Plis","unsupervised, self-supervised, semi-supervised, and supervised representation learning",Accept (Oral),ICLR 2025 Oral,"[6, 6, 8, 8]",7.0,"[3, 3, 3, 3]",3.0,"[3, 3, 4, 3]",3.25,"[4, 3, 4, 3]",3.5,"[3, 3, 4, 4]",3.5,"[""interpretability"", ""fixed points"", ""dynamic routing"", ""dynamic input processing"", ""deep learning framework""]",5,a2812166-c560-4edc-94c2-beb9988ddaad,2024-09-27,0.2703
iclr_k38Th3x4d9,2025,Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery,"Xiao Han, Saima Absar, Lu Zhang, Shuhan Yuan",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8, 8]",8.0,"[2, 3, 3, 3, 4]",3.0,"[3, 3, 3, 3, 4]",3.2,"[3, 3, 3, 3, 4]",3.2,"[3, 3, 3, 4, 4]",3.4,"[""root cause analysis"", ""granger causality"", ""multivariate time series""]",21,5d12afb6-4610-49bc-bd3f-78bd3e5d1ada,2024-09-25,1.1311
iclr_X1OfiRYCLn,2025,Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping,"Yue Yang, Shuibo Zhang, Kaipeng Zhang, Yi Bin, Yu Wang, Ping Luo, Wenqi Shao",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 6]",7.5,"[3, 3, 4, 1]",2.75,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[4, 4, 5, 4]",4.25,"[""dynamic evaluation"", ""vision-language bootstrapping"", ""data contamination"", ""flexible complexity"", ""large vision-language model""]",23,b2ce8b84-f678-445e-9810-5887167c1f28,2024-09-19,1.2256
iclr_bVTM2QKYuA,2025,The Geometry of Categorical and Hierarchical Concepts in Large Language Models,"Kiho Park, Yo Joong Choe, Yibo Jiang, Victor Veitch",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 5]",6.75,"[4, 3, 3, 3]",3.25,"[4, 3, 3, 2]",3.0,"[4, 3, 3, 2]",3.0,"[3, 3, 3, 3]",3.0,"[""categorical concepts"", ""hierarchical concepts"", ""linear representation hypothesis"", ""causal inner product"", ""interpretability""]",126,8ea8a53f-94be-4028-a947-5ef37c5c56fc,2024-06-01,5.6334
iclr_Y6aHdDNQYD,2025,MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection,"Zhuoxiao Chen, Junjie Meng, Mahsa Baktashmotlagh, Yonggang Zhang, Zi Huang, Yadan Luo","transfer learning, meta learning, and lifelong learning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[4, 3, 3]",3.33,"[3, 3, 3]",3.0,"[4, 2, 3]",3.0,"[3, 3, 4]",3.33,"[""test-time adaptation"", ""3d object detection""]",13,821401a4-3f51-4d9b-89a5-c01afc3270e4,2024-06-01,0.5812
iclr_v593OaNePQ,2025,Learning to Search from Demonstration Sequences,"Dixant Mittal, Liwei Kang, Wee Sun Lee",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[10, 8, 8, 6]",8.0,"[4, 2, 3, 2]",2.75,"[4, 4, 3, 3]",3.5,"[3, 3, 4, 2]",3.0,"[4, 4, 4, 3]",3.75,"[""planning"", ""reasoning"", ""learning to search"", ""reinforcement learning"", ""large language model""]",1,3ce946b2-24f9-45f7-af39-fa3512d2b27c,2024-09-28,0.0542
iclr_A3YUPeJTNR,2025,The Hidden Cost of Waiting for Accurate Predictions,"Ali Shirali, Ariel D. Procaccia, Rediet Abebe","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 4, 4, 2]",3.25,"[4, 4, 4, 2]",3.5,"[4, 4, 3, 2]",3.25,"[3, 4, 5, 2]",3.5,"[""algorithmic decision making"", ""prediction"", ""resource allocation"", ""social welfare"", ""limits of prediction""]",2,4917a153-07ce-4049-a7d0-28cd9a9fa8f9,2024-09-25,0.1077
iclr_vzItLaEoDa,2025,Open-World Reinforcement Learning over Long Short-Term Imagination,"Jiajian Li, Qi Wang, Yunbo Wang, Xin Jin, Yang Li, Wenjun Zeng, Xiaokang Yang",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 3]",3.0,"[2, 3, 3, 3]",2.75,"[3, 3, 4, 3]",3.25,"[3, 4, 4, 4]",3.75,"[""world models"", ""reinforcement learning"", ""visual control""]",18,96378fb0-67a3-45c9-9ec4-7bfef5bfa35b,2024-09-13,0.949
iclr_tyEyYT267x,2025,Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models,"Marianne Arriola, Aaron Gokaslan, Justin T Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, Volodymyr Kuleshov",generative models,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 2]",3.0,"[3, 3, 4, 3]",3.25,"[3, 4, 3, 2]",3.0,"[""diffusion models"", ""text diffusion"", ""generative models""]",279,c97e6d17-1293-4b26-908a-65f32a81a0aa,2024-09-27,15.0811
iclr_HvSytvg3Jh,2025,AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models,"Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Jie Shi, Xiang Wang, Xiangnan He, Tat-Seng Chua","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 4]",3.25,"[4, 3, 3, 4]",3.5,"[4, 3, 3, 4]",3.5,"[3, 3, 3, 4]",3.25,"[""model editing"", ""null-space"", ""large language model""]",178,5056586a-0ea0-40ac-89f8-3dbe62b01e2f,2024-09-24,9.5699
iclr_XmProj9cPs,2025,Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows,"Fangyu Lei, Jixuan Chen, Yuxiao Ye, Ruisheng Cao, Dongchan Shin, Hongjin SU, ZHAOQING SUO, Hongcheng Gao, Wenjing Hu, Pengcheng Yin, Victor Zhong, Caiming Xiong, Ruoxi Sun, Qian Liu, Sida Wang, Tao Yu",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 4, 4]",3.75,"[4, 3, 4, 3]",3.5,"[3, 4, 4, 4]",3.75,"[4, 5, 4, 5]",4.5,"[""llm benchmark"", ""data science and engineering"", ""code generation"", ""text-to-sql"", ""llm agent""]",209,5f2b789d-e5c7-4ae8-9086-3c106e49c78a,2024-09-28,11.3177
iclr_N8Oj1XhtYZ,2025,SANA: Efficient High-Resolution Text-to-Image Synthesis with Linear Diffusion Transformers,"Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, Song Han",generative models,Accept (Oral),ICLR 2025 Oral,"[10, 8, 8, 8]",8.5,"[4, 3, 3, 3]",3.25,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 2]",2.75,"[5, 4, 4, 4]",4.25,"[""efficient ai"", ""diffusion models"", ""text to image generation""]",83,7b1e834b-6c9d-4b62-bcfa-5e9b22ce03fc,2024-09-19,4.4227
iclr_Bo62NeU6VF,2025,Backtracking Improves Generation Safety,"Yiming Zhang, Jianfeng Chi, Hailey Nguyen, Kartikeya Upasani, Daniel M. Bikel, Jason E Weston, Eric Michael Smith","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 4, 4, 3]",3.75,"[4, 4, 3, 3]",3.5,"[4, 3, 3, 3]",3.25,"[2, 4, 4, 4]",3.5,"[""ai safety"", ""generation algorithm"", ""backtracking""]",42,71b2cd1c-91bd-4b72-9dc5-1c3adb5cdc52,2024-09-01,2.1687
iclr_NO6Tv6QcDs,2025,Limits to scalable evaluation at the frontier: LLM as judge won’t beat twice the data,"Florian E. Dorner, Vivian Yvonne Nastl, Moritz Hardt",learning theory,Accept (Oral),ICLR 2025 Oral,"[6, 6, 8, 6]",6.5,"[2, 2, 4, 3]",2.75,"[3, 3, 4, 3]",3.25,"[3, 4, 4, 3]",3.5,"[2, 4, 4, 3]",3.25,"[""evaluation"", ""benchmarking"", ""model-as-a-judge"", ""theory""]",31,cc5ffc7a-9347-421d-ac68-f137e33df87f,2024-09-27,1.6757
iclr_eHehzSDUFp,2025,Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition,"Jiyeon Kim, Hyunji Lee, Hyowon Cho, Joel Jang, Hyeonbin Hwang, Seungpil Won, Youbin Ahn, Dohaeng Lee, Minjoon Seo","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[5, 8, 8, 8]",7.25,"[2, 4, 2, 3]",2.75,"[2, 4, 3, 3]",3.0,"[2, 4, 3, 3]",3.0,"[4, 4, 3, 4]",3.75,"[""knowledge entropy"", ""knowledge acquisition and forgetting"", ""evolving behavior during llm pretraining""]",20,57a20e4d-5304-437b-bfaf-dfdb1f830f6e,2024-09-28,1.083
iclr_cNmu0hZ4CL,2025,Comparing noisy neural population dynamics using optimal transport distances,"Amin Nejatbakhsh, Victor Geadah, Alex H Williams, David Lipshutz",applications to neuroscience & cognitive science,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 4, 4]",3.67,"[3, 3, 3]",3.0,"[4, 3, 3]",3.33,"[4, 4, 3]",3.67,"[""representational similarity"", ""shape metrics"", ""optimal transport"", ""wasserstein distance""]",5,1694ade4-b5bf-4250-9e17-94cc2df9e41b,2024-09-24,0.2688
iclr_eFGQ97z5Cd,2025,Your Mixture-of-Experts LLM Is Secretly an Embedding Model for Free,"Ziyue Li, Tianyi Zhou","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[6, 6, 8]",6.67,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[2, 2, 3]",2.33,"[3, 3, 4]",3.33,"[""mixture of experts""]",33,6d17bec2-8346-4662-99c1-211d1995f1ee,2024-09-21,1.7647
iclr_Xo0Q1N7CGk,2025,On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding,"Dehong Xu, Ruiqi Gao, Wenhao Zhang, Xue-Xin Wei, Ying Nian Wu","unsupervised, self-supervised, semi-supervised, and supervised representation learning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 3]",3.0,"[4, 3, 3, 3]",3.25,"[3, 3, 2, 3]",2.75,"[4, 3, 3, 4]",3.5,"[""grid cells"", ""conformal isometry"", ""distance-preserving"", ""position embedding"", ""representation learning""]",11,e61c20c5-6d21-474c-95d6-269232ba9395,2024-05-01,0.4708
iclr_wM2sfVgMDH,2025,Diffusion-Based Planning for Autonomous Driving with Flexible Guidance,"Yinan Zheng, Ruiming Liang, Kexin ZHENG, Jinliang Zheng, Liyuan Mao, Jianxiong Li, Weihao Gu, Rui Ai, Shengbo Eben Li, Xianyuan Zhan, Jingjing Liu","applications to robotics, autonomy, planning",Accept (Oral),ICLR 2025 Oral,"[6, 8, 8, 8]",7.5,"[3, 3, 3, 2]",2.75,"[3, 4, 3, 3]",3.25,"[2, 4, 3, 3]",3.0,"[4, 4, 3, 3]",3.5,"[""diffusion planning"", ""autonomous driving""]",130,9bbd2f62-b226-4d8c-807e-090ff3aa0fe6,2024-09-28,7.0397
iclr_meRCKuUpmc,2025,Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation,"Yang Tian, Sizhe Yang, Jia Zeng, Ping Wang, Dahua Lin, Hao Dong, Jiangmiao Pang","applications to robotics, autonomy, planning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 6]",7.5,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 3]",2.75,"[4, 4, 4, 4]",4.0,"[""robotic manipulation ; pre-training ; visual foresight ; inverse dynamics ; large-scale robot dataset""]",116,fb2c9752-70eb-4b33-8500-1c8191608f72,2024-09-26,6.259
iclr_syThiTmWWm,2025,Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates,"Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Jing Jiang, Min Lin","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 10, 10, 8, 6, 6, 6, 8]",7.75,"[4, 4, 3, 3, 4, 2, 4, 3]",3.38,"[4, 4, 3, 3, 4, 3, 3, 4]",3.5,"[4, 4, 3, 2, 3, 3, 2, 4]",3.12,"[4, 4, 4, 3, 4, 3, 3, 3]",3.5,"[""large language models"", ""cheating"", ""automatic llm benchmarks""]",29,36583f84-bc3d-4c84-8c2e-50938649bef8,2024-09-26,1.5647
iclr_xoIeVdFO7U,2025,Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning,"Chongyi Zheng, Jens Tuyls, Joanne Peng, Benjamin Eysenbach",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[4, 3, 4, 4]",3.75,"[4, 3, 4, 4]",3.75,"[4, 3, 2, 4]",3.25,"[2, 3, 3, 3]",2.75,"[""unsupervised learning"", ""reinforcement learning"", ""mutual information"", ""successor feature""]",18,c3a7bf73-4690-40a7-ad65-40644303e97e,2024-09-17,0.9558
iclr_zCxGCdzreM,2025,Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks,"Michael Matthews, Michael Beukman, Chris Lu, Jakob Nicolaus Foerster",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 4]",3.5,"[3, 2, 3, 3]",2.75,"[3, 2, 3, 4]",3.0,"[3, 4, 4, 3]",3.5,"[""reinforcement learning"", ""open-endedness"", ""unsupervised environment design"", ""automatic curriculum learning"", ""benchmark""]",21,f02c1d8c-40ef-4a49-abbd-22de96ae968f,2024-09-27,1.1351
iclr_zl0HLZOJC9,2025,Probabilistic Learning to Defer: Handling Missing Expert Annotations and Controlling Workload Distribution,"Cuong C. Nguyen, Thanh-Toan Do, Gustavo Carneiro","other topics in machine learning (i.e., none of the above)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 2, 3, 2]",2.5,"[""learning to defer"", ""expectation - maximisation""]",10,c37aa1d3-af96-417c-88a0-99cc3ba9c4bc,2024-09-13,0.5272
iclr_h0Ak8A5yqw,2025,On the Role of Attention Heads in Large Language Model Safety,"Zhenhong Zhou, Haiyang Yu, Xinghua Zhang, Rongwu Xu, Fei Huang, Kun Wang, Yang Liu, Junfeng Fang, Yongbin Li","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[6, 8, 6, 8]",7.0,"[2, 3, 2, 4]",2.75,"[2, 3, 2, 4]",2.75,"[3, 3, 2, 3]",2.75,"[4, 3, 3, 4]",3.5,"[""interpretability"", ""large language model"", ""multi-head attention"", ""safety"", ""harmful content""]",63,555bd23e-c1e3-43e1-b6b4-f2b596198aa5,2024-09-24,3.3871
iclr_0ctvBgKFgc,2025,ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids,"Hannes Stark, Bowen Jing, Tomas Geffner, Jason Yim, Tommi Jaakkola, Arash Vahdat, Karsten Kreis","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 4, 4, 3]",3.75,"[4, 3, 4, 3]",3.5,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 4]",3.5,"[""protein design"", ""diffusion model"", ""controllable generation"", ""drug discovery"", ""proteins"", ""biology""]",7,18f49a17-7705-4fb5-bcd1-423bde98947f,2024-09-25,0.377
iclr_GGlpykXDCa,2025,MMQA: Evaluating LLMs with Multi-Table Multi-Hop Complex Questions,"Jian Wu, Linyi Yang, Dongyuan Li, Yuliang Ji, Manabu Okumura, Yue Zhang",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[4, 3, 3]",3.33,"[4, 3, 4]",3.67,"[4, 3, 3]",3.33,"[4, 5, 4]",4.33,"[""llm evaluation"", ""multi-table question answering; multi-hop question answering""]",34,de7beb00-83ea-405b-9849-37d45ba8375b,2024-09-26,1.8345
iclr_Ha6RTeWMd0,2025,SAM 2: Segment Anything in Images and Videos,"Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dollar, Christoph Feichtenhofer","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[10, 8, 8, 10]",9.0,"[4, 3, 3, 4]",3.5,"[4, 4, 3, 4]",3.75,"[3, 4, 3, 4]",3.5,"[4, 4, 4, 5]",4.25,"[""computer vision"", ""video segmentation"", ""image segmentation""]",3970,bd604f95-7463-4e6c-b0cc-e87f58a6fe76,2024-08-01,194.9264
iclr_Cjz9Xhm7sI,2025,High-Dynamic Radar Sequence Prediction for Weather Nowcasting Using Spatiotemporal Coherent Gaussian Representation,"Ziye Wang, Yiran Qin, Lin Zeng, Ruimao Zhang","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[3, 3, 2]",2.67,"[2, 4, 4]",3.33,"[""3d gaussian"", ""dynamic reconstruction"", ""radar prediction"", ""weather nowcasting""]",10,d5994a77-5760-43d5-b36a-b1bfb0f0c469,2024-09-22,0.5357
iclr_rFpZnn11gj,2025,PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration,"Yuxuan Sun, Yunlong Zhang, Yixuan Si, Chenglu Zhu, Kai Zhang, Zhongyi Shui, Jingxiong Li, Xuan Gong, XINHENG LYU, Tao Lin, Lin Yang",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 6]",7.5,"[3, 3, 3, 3]",3.0,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[5, 4, 5, 5]",4.75,"[""image-text pairs generation"", ""vision-language models"", ""multi-agent collaboration""]",41,0707ad97-c856-4508-a66d-3537f4644d8f,2024-07-01,1.9189
iclr_GMwRl2e9Y1,2025,Restructuring Vector Quantization with the Rotation Trick,"Christopher Fifty, Ronald Guenther Junkins, Dennis Duan, Aniketh Iyengar, Jerry Weihong Liu, Ehsan Amid, Sebastian Thrun, Christopher Re",generative models,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 4]",3.5,"[4, 4, 3, 4]",3.75,"[3, 3, 3, 3]",3.0,"[4, 4, 3, 4]",3.75,"[""vector quantization"", ""vq-vae""]",46,6d9a036b-1a7b-4ba0-beb7-2c70b465f1fb,2024-09-27,2.4865
iclr_5UKrnKuspb,2025,NeuralPlane: Structured 3D Reconstruction in Planar Primitives with Neural Fields,"Hanqiao Ye, Yuzhou Liu, Yangdong Liu, Shuhan Shen","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 6, 10, 8]",8.0,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 3]",3.25,"[5, 3, 4, 4]",4.0,"[""3d reconstruction"", ""3d scene understanding"", ""scene abstraction"", ""neural rendering""]",10,2c17eb6d-bec3-4ae6-aa37-4749059ae75f,2024-09-23,0.5367
iclr_ilOEOIqolQ,2025,AI as Humanity’s Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text,"Ximing Lu, Melanie Sclar, Skyler Hallinan, Niloofar Mireshghallah, Jiacheng Liu, Seungju Han, Allyson Ettinger, Liwei Jiang, Khyathi Chandu, Nouha Dziri, Yejin Choi",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 6]",7.0,"[4, 4, 4, 4]",4.0,"[4, 3, 3, 2]",3.0,"[3, 3, 4, 2]",3.0,"[3, 5, 3, 5]",4.0,"[""machine creativity"", ""large language model"", ""science of llm"", ""machine text detection""]",13,a93db208-ff67-41e0-b6c2-6c373b5d00fe,2024-09-24,0.6989
iclr_zBbZ2vdLzH,2025,Joint Graph Rewiring and Feature Denoising via Spectral Resonance,"Jonas Linkerhägner, Cheng Shi, Ivan Dokmanić",learning on graphs and other geometries & topologies,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8, 8]",8.0,"[4, 3, 3, 3, 2]",3.0,"[4, 3, 3, 3, 3]",3.2,"[4, 3, 2, 3, 2]",2.8,"[3, 2, 3, 3, 3]",2.8,"[""gnns"", ""rewiring"", ""denoising"", ""spectral resonance"", ""csbm""]",7,82bbaab0-703a-4369-9b6a-1c0587c612f2,2024-08-01,0.3437
iclr_cmfyMV45XO,2025,Feedback Favors the Generalization of Neural ODEs,"Jindou Jia, Zihan Yang, Meng Wang, Kexin Guo, Jianfei Yang, Xiang Yu, Lei Guo",learning on time series and dynamical systems,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 2]",2.75,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 3]",3.25,"[""neural odes"", ""feedback"", ""generalization"", ""learning dynamical systems"", ""model predictive control""]",4,75c81831-9231-4f95-9fc1-e5e63f519d69,2024-09-27,0.2162
iclr_WOzffPgVjF,2025,Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding,"Xin Gu, Yaojie Shen, Chenxi Luo, Tiejian Luo, Yan Huang, Yuewei Lin, Heng Fan, Libo Zhang","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[4, 3, 3, 3]",3.25,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[5, 3, 4, 4]",4.0,"[""spatio-temporal video grounding""]",10,22a1a87e-47c4-4faa-bf73-b2c4dec59744,2024-09-13,0.5272
iclr_5IkDAfabuo,2025,Prioritized Generative Replay,"Renhao Wang, Kevin Frans, Pieter Abbeel, Sergey Levine, Alexei A Efros",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[3, 3, 3, 3]",3.0,"[3, 4, 3, 4]",3.5,"[3, 4, 3, 3]",3.25,"[4, 3, 3, 4]",3.5,"[""online learning"", ""model-based reinforcement learning"", ""generative modeling"", ""synthetic data"", ""continual learning""]",18,cd8c5973-7dd9-413c-9058-9731bd37cd04,2024-09-23,0.966
iclr_RWJX5F5I9g,2025,Brain Bandit: A Biologically Grounded Neural Network for Efficient Control of Exploration,"Chen Jiang, Jiahui An, Yating Liu, Ni Ji",applications to neuroscience & cognitive science,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 2, 3]",2.67,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[3, 3, 4]",3.33,"[""explore-exploit"", ""stochastic hopfield network"", ""thompson sampling"", ""decision under uncertainty"", ""brain-inspired algorithm"", ""reinforcement learning""]",0,1b3517d8-9e53-43e0-ada4-9dbaa53ec703,2024-09-28,0.0
iclr_o2Igqm95SJ,2025,CAX: Cellular Automata Accelerated in JAX,"Maxence Faldor, Antoine Cully","infrastructure, software libraries, hardware, systems, etc.",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 4, 4, 3]",3.5,"[2, 3, 3, 4]",3.0,"[3, 2, 2, 4]",2.75,"[4, 3, 3, 5]",3.75,"[""cellular automata"", ""emergence"", ""self-organization"", ""neural cellular automata""]",9,6459d11f-76a5-4521-86a0-e7ccc352d52f,2024-09-27,0.4865
iclr_RuP17cJtZo,2025,Generator Matching: Generative modeling with arbitrary Markov processes,"Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi Jaakkola, Brian Karrer, Ricky T. Q. Chen, Yaron Lipman",generative models,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 4, 3, 4]",3.5,"[4, 3, 3, 4]",3.5,"[4, 3, 3, 4]",3.5,"[3, 4, 2, 4]",3.25,"[""flow matching"", ""markov process"", ""diffusion model"", ""generative modeling""]",63,42529ea4-7076-4c51-9623-9fa3b0218e16,2024-09-23,3.381
iclr_eIJfOIMN9z,2025,Language Representations Can be What Recommenders Need: Findings and Potentials,"Leheng Sheng, An Zhang, Yi Zhang, Yuxin Chen, Xiang Wang, Tat-Seng Chua","other topics in machine learning (i.e., none of the above)",Accept (Oral),ICLR 2025 Oral,"[8, 5, 8, 8]",7.25,"[4, 3, 4, 4]",3.75,"[4, 3, 3, 2]",3.0,"[4, 3, 3, 3]",3.25,"[2, 5, 4, 5]",4.0,"[""collaborative filtering"", ""language-representation-based recommendation"", ""language models"", ""language model representations""]",48,3f6d6704-d77e-47fe-a9be-5970cc164d57,2024-07-01,2.2465
iclr_mtSSFiqW6y,2025,Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment,"Gregor Bachmann, Sotiris Anagnostidis, Albert Pumarola, Markos Georgopoulos, Artsiom Sanakoyeu, Yuming Du, Edgar Schönfeld, Ali Thabet, Jonas K Kohler","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 10, 6]",8.0,"[4, 4, 4, 3]",3.75,"[4, 3, 3, 3]",3.25,"[4, 3, 4, 2]",3.25,"[3, 4, 4, 3]",3.5,"[""llm inference"", ""speculative decoding""]",39,02a5fd10-b5bd-410e-820e-f0d7a9dae32c,2024-09-25,2.1005
iclr_vaEPihQsAA,2025,CyberHost: A One-stage Diffusion Framework for Audio-driven Talking Body Generation,"Gaojie Lin, Jianwen Jiang, Chao Liang, Tianyun Zhong, Jiaqi Yang, Zerong Zheng, Yanbo Zheng","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 6, 6, 10, 8]",7.6,"[3, 2, 3, 3, 3]",2.8,"[3, 3, 3, 3, 3]",3.0,"[4, 3, 3, 3, 3]",3.2,"[4, 4, 4, 5, 3]",4.0,"[""audio-driven human animation.+diffusion model.+generative model.+human video generation""]",19,7feb19de-8bdf-48b3-8d17-3ff521ad4499,2024-09-25,1.0233
iclr_QQBPWtvtcn,2025,LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias,"Haian Jin, Hanwen Jiang, Hao Tan, Kai Zhang, Sai Bi, Tianyuan Zhang, Fujun Luan, Noah Snavely, Zexiang Xu","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8, 8, 8]",7.67,"[4, 3, 3, 3, 3, 3]",3.17,"[4, 4, 3, 3, 4, 4]",3.67,"[4, 4, 2, 2, 3, 3]",3.0,"[5, 5, 5, 4, 4, 4]",4.5,"[""novel view synthesis"", ""transformer"", ""large model""]",105,6f04b320-7550-4956-a620-cedc12c742a9,2024-09-17,5.5752
iclr_51WraMid8K,2025,A Probabilistic Perspective on Unlearning and Alignment for Large Language Models,"Yan Scholten, Stephan Günnemann, Leo Schwinn","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[10, 6, 10, 6]",8.0,"[3, 2, 3, 2]",2.5,"[4, 3, 4, 3]",3.5,"[4, 3, 3, 3]",3.25,"[3, 3, 2, 3]",2.75,"[""machine unlearning"", ""alignment"", ""large language models""]",30,80cc0007-0a6a-42d2-9eb7-3d00088b5a94,2024-09-26,1.6187
iclr_84pDoCD4lH,2025,Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference under Ambiguities,"Zheyuan Zhang, Fengyuan Hu, Jayjun Lee, Freda Shi, Parisa Kordjamshidi, Joyce Chai, Ziqiao Ma","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[10, 8, 8, 6, 5]",7.4,"[4, 4, 3, 3, 3]",3.4,"[4, 4, 4, 4, 3]",3.8,"[4, 3, 3, 3, 3]",3.2,"[4, 4, 4, 3, 3]",3.6,"[""vision-language models"", ""spatial reasoning"", ""multimodal reasoning""]",0,39f9c544-9aaa-4e2a-a910-b033fb6e3418,2024-09-13,0.0
iclr_AP0ndQloqR,2025,Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces,"Saket Tiwari, Omer Gottesman, George Konidaris",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 6]",7.5,"[3, 3, 3, 2]",2.75,"[4, 3, 3, 2]",3.0,"[4, 3, 3, 3]",3.25,"[2, 2, 3, 3]",2.5,"[""reinforcement learning"", ""deep learning"", ""geometry""]",4,d55f2bfe-0af3-4a49-8999-72a227740a01,2024-09-26,0.2158
iclr_weM4YBicIP,2025,Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency,"Jianwen Jiang, Chao Liang, Jiaqi Yang, Gaojie Lin, Tianyun Zhong, Yanbo Zheng","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 4]",3.5,"[4, 4, 4, 4]",4.0,"[4, 4, 3, 3]",3.5,"[5, 4, 4, 4]",4.25,"[""diffusion model"", ""avatar"", ""portrait animation"", ""audio-condition video generation""]",103,a160b609-0ffb-45a3-961b-c86c7dc2b406,2024-09-01,5.3184
iclr_ZuazHmXTns,2025,Problem-Parameter-Free Federated Learning,"Wenjing Yan, Kai Zhang, Xiaolu Wang, Xuanyu Cao",optimization,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8, 6]",7.6,"[3, 3, 2, 3, 3]",2.8,"[3, 2, 3, 3, 3]",2.8,"[3, 3, 3, 2, 3]",2.8,"[4, 3, 3, 3, 4]",3.4,"[""adaptive federated learning"", ""problem-parameter free"", ""arbitrary data heterogeneity"", ""adaptive stepsize""]",10,a2393f43-1e91-4db5-bb10-4f51dc627913,2024-09-26,0.5396
iclr_XFYUwIyTxQ,2025,EmbodiedSAM: Online Segment Any 3D Thing in Real Time,"Xiuwei Xu, Huangxing Chen, Linqing Zhao, Ziwei Wang, Jie Zhou, Jiwen Lu","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8, 8, 8]",7.67,"[2, 3, 3, 3, 3, 4]",3.0,"[3, 3, 3, 3, 3, 3]",3.0,"[3, 2, 3, 3, 3, 3]",2.83,"[2, 4, 4, 2, 4, 5]",3.5,"[""3d instance segmentation; online 3d scene segmentation""]",48,35d8528f-8f42-4301-bcf0-312aa361489d,2024-08-01,2.3568
iclr_2efNHgYRvM,2025,On the Identification of Temporal Causal Representation with Instantaneous Dependence,"Zijian Li, Yifan Shen, Kaitao Zheng, Ruichu Cai, Xiangchen Song, Mingming Gong, Guangyi Chen, Kun Zhang","unsupervised, self-supervised, semi-supervised, and supervised representation learning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[2, 3, 3]",2.67,"[4, 2, 3]",3.0,"[""causal representation learning"", ""instantaneous dependency"", ""identification""]",18,30c9b6a3-0b35-415c-81e7-0ca0572e56b3,2024-05-01,0.7703
iclr_3IFRygQKGL,2025,OptionZero: Planning with Learned Options,"Po-Wei Huang, Pei-Chiun Peng, Hung Guei, Ti-Rong Wu",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 4]",3.25,"[4, 3, 2, 4]",3.25,"[3, 3, 4, 4]",3.5,"[""option"", ""semi-mdp"", ""muzero"", ""mcts"", ""planning"", ""reinforcement learning""]",3,76a83648-952b-422a-bf90-2b8a91580282,2024-09-27,0.1622
iclr_I4e82CIDxv,2025,Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models,"Samuel Marks, Can Rager, Eric J Michaud, Yonatan Belinkov, David Bau, Aaron Mueller",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 4]",3.25,"[3, 4, 3, 3]",3.25,"[3, 3, 4, 4]",3.5,"[3, 5, 3, 3]",3.5,"[""interpretability"", ""mechanistic interpretability"", ""circuits"", ""spurious correlations"", ""generalization"", ""dictionary learning""]",292,b22508fe-c7a6-4769-8e0f-848a4da96d9d,2024-03-01,11.5112
iclr_EUSkm2sVJ6,2025,How much of my dataset did you use? Quantitative Data Usage Inference in Machine Learning,"Yao Tong, Jiayuan Ye, Sajjad Zarifzadeh, Reza Shokri","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8, 6]",7.6,"[4, 3, 3, 3, 3]",3.2,"[3, 3, 3, 3, 3]",3.0,"[4, 3, 3, 4, 2]",3.2,"[4, 3, 2, 3, 2]",2.8,"[""machine learning"", ""privacy"", ""dataset usage inference"", ""dataset ownership"", ""membership inference attack"", ""dataset copyright""]",9,a1da8bc2-e518-4036-a387-81b6e7615424,2024-09-26,0.4856
iclr_UvTo3tVBk2,2025,Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues,"Riccardo Grazzi, Julien Siems, Arber Zela, Jörg K.H. Franke, Frank Hutter, Massimiliano Pontil","other topics in machine learning (i.e., none of the above)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[4, 3, 4]",3.67,"[4, 3, 4]",3.67,"[3, 4, 4]",3.67,"[3, 4, 4]",3.67,"[""state tracking"", ""state space"", ""mamba"", ""linear rnn"", ""linear attention"", ""gla"", ""deltanet"", ""formal languages"", ""products of householders""]",59,2acfc7dd-2a28-481b-bbd0-8c976568e23b,2024-09-27,3.1892
iclr_DzGe40glxs,2025,Interpreting Emergent Planning in Model-Free Reinforcement Learning,"Thomas Bush, Stephen Chung, Usman Anwar, Adrià Garriga-Alonso, David Krueger",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 3]",3.25,"[4, 3, 2, 3]",3.0,"[3, 3, 3, 4]",3.25,"[4, 3, 4, 4]",3.75,"[""reinforcement learning"", ""interpretability"", ""planning"", ""probes"", ""model-free"", ""mechanistic interpretability"", ""sokoban""]",16,34e00a09-e98b-4247-b028-328469d55bb5,2024-09-27,0.8649
iclr_Pujt3ADZgI,2025,Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning,"Yuheng Zhang, Dian Yu, Baolin Peng, Linfeng Song, Ye Tian, Mingyue Huo, Nan Jiang, Haitao Mi, Dong Yu",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[6, 6, 6, 6]",6.0,"[3, 3, 3, 2]",2.75,"[3, 3, 2, 4]",3.0,"[3, 3, 2, 3]",2.75,"[5, 4, 4, 2]",3.75,"[""rlhf theory"", ""llm alignment""]",49,5fecf912-4c84-4b8e-b529-c1982aad5186,2024-07-01,2.2933
iclr_1CLzLXSFNn,2025,TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis,"Shiyu Wang, Jiawei LI, Xiaoming Shi, Zhou Ye, Baichuan Mo, Wenze Lin, Ju Shengtong, Zhixuan Chu, Ming Jin",learning on time series and dynamical systems,Accept (Oral),ICLR 2025 Oral,"[10, 8, 6]",8.0,"[4, 3, 3]",3.33,"[4, 3, 2]",3.0,"[4, 4, 2]",3.33,"[4, 5, 4]",4.33,"[""time series"", ""pattern machine"", ""predictive analysis""]",169,200e12a0-84c6-4966-87fd-a4431ce5455f,2024-09-27,9.1351
iclr_SnDmPkOJ0T,2025,REEF: Representation Encoding Fingerprints for Large Language Models,"Jie Zhang, Dongrui Liu, Chen Qian, Linfeng Zhang, Yong Liu, Yu Qiao, Jing Shao","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[10, 8, 6, 8]",8.0,"[4, 3, 3, 3]",3.25,"[4, 3, 2, 4]",3.25,"[4, 3, 3, 3]",3.25,"[4, 3, 2, 4]",3.25,"[""large language model"", ""fingerprint"", ""representation"", ""intellectual property""]",66,9e164642-7932-4f8a-8f08-ea43e5113738,2024-09-21,3.5294
iclr_HnhNRrLPwm,2025,MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models,"Peng Xia, Siwei Han, Shi Qiu, Yiyang Zhou, Zhaoyang Wang, Wenhao Zheng, Zhaorun Chen, Chenhang Cui, Mingyu Ding, Linjie Li, Lijuan Wang, Huaxiu Yao",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 4, 3]",3.33,"[3, 4, 3]",3.33,"[3, 3, 2]",2.67,"[4, 4, 4]",4.0,"[""large vision-language model"", ""interleaved text-and-image evaluation""]",42,2e27c97c-18ee-44cd-8317-3cc7f9bd6484,2024-09-15,2.2222
iclr_wPMRwmytZe,2025,Progressive distillation induces an implicit curriculum,"Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel","unsupervised, self-supervised, semi-supervised, and supervised representation learning",Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 8, 8]",7.6,"[3, 3, 4, 4, 4]",3.6,"[3, 4, 4, 3, 4]",3.6,"[3, 3, 3, 4, 3]",3.2,"[3, 4, 4, 4, 3]",3.6,"[""knowledge distillation"", ""feature learning"", ""curriculum"", ""sparse parity"", ""pcfg"", ""optimization"", ""mlp"", ""transformer""]",15,cf47cce3-c4a4-4314-950d-7bae4869eeef,2024-09-26,0.8094
iclr_NxyfSW6mLK,2025,REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments,"Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman, Insup Lee",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[6, 8, 5, 8]",6.75,"[2, 3, 3, 2]",2.5,"[3, 3, 2, 3]",2.75,"[3, 3, 2, 3]",2.75,"[3, 4, 3, 5]",3.75,"[""generalist agent"", ""retrieval"", ""in-context learning"", ""vla"", ""imitation learning"", ""reinforcement learning""]",17,5f671e7e-1c63-4124-8498-4ff4b385adbc,2024-09-15,0.8995
iclr_xXTkbTBmqq,2025,OLMoE: Open Mixture-of-Experts Language Models,"Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob Morrison, Sewon Min, Weijia Shi, Evan Pete Walsh, Oyvind Tafjord, Nathan Lambert, Yuling Gu, Shane Arora, Akshita Bhagia, Dustin Schwenk, David Wadden, Alexander Wettig, Binyuan Hui, Tim Dettmers, Douwe Kiela, Ali Farhadi, Noah A. Smith, Pang Wei Koh, Amanpreet Singh, Hannaneh Hajishirzi","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 10]",8.67,"[4, 4, 4]",4.0,"[4, 4, 4]",4.0,"[4, 3, 4]",3.67,"[3, 2, 5]",3.33,"[""large language models"", ""mixture-of-experts"", ""open-source""]",156,34c991e4-d035-4fa8-b361-f9855db49ab3,2024-09-01,8.0551
iclr_tcsZt9ZNKD,2025,Scaling and evaluating sparse autoencoders,"Leo Gao, Tom Dupre la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, Jeffrey Wu",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[3, 10, 10, 8, 10]",8.2,"[3, 4, 3, 4, 3]",3.4,"[3, 4, 4, 3, 4]",3.6,"[2, 4, 4, 4, 4]",3.6,"[4, 4, 4, 4, 4]",4.0,"[""interpretability"", ""sparse autoencoders"", ""superposition"", ""scaling laws""]",514,8e0f25c3-b149-4bff-83a8-b5d5ae2e0a7d,2024-06-01,22.9806
iclr_m2nmp8P5in,2025,LLM-SR: Scientific Equation Discovery via Programming with Large Language Models,"Parshin Shojaee, Kazem Meidani, Shashank Gupta, Amir Barati Farimani, Chandan K. Reddy","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 3]",3.25,"[4, 4, 3, 3]",3.5,"[4, 3, 3, 3]",3.25,"[3, 4, 4, 3]",3.5,"[""symbolic regression"", ""equation discovery"", ""large language models"", ""evolutionary search""]",158,15fdfa75-f7e5-4759-8c6d-daa5f974229e,2024-04-01,6.4843
iclr_tcvMzR2NrP,2025,Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective,"Neta Shaul, Itai Gat, Marton Havasi, Daniel Severo, Anuroop Sriram, Peter Holderrieth, Brian Karrer, Yaron Lipman, Ricky T. Q. Chen",generative models,Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 10, 6]",7.6,"[3, 3, 4, 4, 3]",3.4,"[3, 3, 4, 4, 3]",3.4,"[3, 3, 4, 4, 3]",3.4,"[2, 3, 2, 2, 3]",2.4,"[""flow matching"", ""discrete generative modeling""]",44,463bb300-4631-44ba-a78a-8fd062a2a0d1,2024-09-17,2.3363
iclr_SI2hI0frk6,2025,Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model,"Chunting Zhou, LILI YU, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, Omer Levy","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 8, 8]",7.6,"[3, 3, 3, 4, 3]",3.2,"[3, 3, 4, 3, 2]",3.0,"[3, 3, 3, 3, 4]",3.2,"[3, 5, 4, 4, 4]",4.0,"[""multimodal foundation model"", ""multimodal generation and understanding"", ""diffusion"", ""next token prediction""]",491,dcdc41ff-aac1-4870-b82d-4bcad00fa840,2024-08-01,24.108
iclr_hyfe5q5TD0,2025,Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics,"Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 4, 3, 3]",3.5,"[4, 3, 3, 3]",3.25,"[4, 3, 3, 4]",3.5,"[4, 4, 3, 4]",3.75,"[""reinforcement learning theory"", ""linear function approximation""]",10,374f8aac-60e6-4d63-a05b-ea4082fdda10,2024-06-01,0.4471
iclr_SctfBCLmWo,2025,A Decade's Battle on Dataset Bias: Are We There Yet?,"Zhuang Liu, Kaiming He",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 3, 4]",3.33,"[4, 4, 3]",3.67,"[3, 3, 3]",3.0,"[4, 4, 4]",4.0,"[""vision datasets"", ""dataset bias"", ""deep learning""]",84,b9b72d9f-f495-42cc-a243-648a1ca2ec99,2024-03-01,3.3114
iclr_mtJSMcF3ek,2025,Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models,"Yuda Song, Hanlin Zhang, Carson Eisenach, Sham M. Kakade, Dean Foster, Udaya Ghai","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[6, 8, 8, 6]",7.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[4, 4, 3, 4]",3.75,"[""llm"", ""self-improvement"", ""synthetic data"", ""post-training"", ""test-time optimization""]",60,06074ed1-c0f1-4954-81ff-c08602e5fc24,2024-09-19,3.1972
iclr_FIj9IEPCKr,2025,Proxy Denoising for Source-Free Domain Adaptation,"Song Tang, Wenxin Su, Yan Gan, Mao Ye, Jianwei Dr. Zhang, Xiatian Zhu","transfer learning, meta learning, and lifelong learning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 3]",2.75,"[4, 4, 4, 3]",3.75,"[""domain adaptation"", ""source-free"", ""multimodal proxy space"", ""proxy confidence theory""]",18,b905791f-01e1-41bd-8dfa-2cc18dba61e5,2024-06-01,0.8048
iclr_rdv6yeMFpn,2025,Homomorphism Expressivity of Spectral Invariant Graph Neural Networks,"Jingchu Gai, Yiheng Du, Bohang Zhang, Haggai Maron, Liwei Wang",learning on graphs and other geometries & topologies,Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 6, 10]",7.6,"[3, 3, 3, 3, 4]",3.2,"[4, 2, 3, 3, 4]",3.2,"[4, 2, 2, 3, 4]",3.0,"[4, 4, 4, 1, 5]",3.6,"[""graph neural network"", ""expressive power"", ""spectral invariant"", ""graph homomorphism"", ""weisfeiler-lehman""]",7,2bbd3ab8-5ebd-43c2-a34c-2dbc9fd1fb33,2024-09-27,0.3784
iclr_PSiijdQjNU,2025,Steering Protein Family Design through Profile Bayesian Flow,"Jingjing Gong, Yu Pei, Siyu Long, Yuxuan Song, Zhe Zhang, Wenhao Huang, Ziyao Cao, Shuyi Zhang, Hao Zhou, Wei-Ying Ma","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[6, 8, 8, 8]",7.5,"[2, 3, 2, 3]",2.5,"[2, 3, 3, 3]",2.75,"[2, 3, 4, 4]",3.25,"[4, 3, 3, 3]",3.25,"[""protein family generation"", ""homologous protein generation"", ""protein design"", ""bayesian flow""]",6,934b91b8-53bf-4337-a5d5-34f554399590,2024-09-27,0.3243
iclr_3i13Gev2hV,2025,Compositional Entailment Learning for Hyperbolic Vision-Language Models,"Avik Pal, Max van Spengler, Guido Maria D'Amely di Melendugno, Alessandro Flaborea, Fabio Galasso, Pascal Mettes","unsupervised, self-supervised, semi-supervised, and supervised representation learning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 4]",3.5,"[3, 4, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 4, 3, 3]",3.25,"[""vision-language models"", ""hyperbolic geometry"", ""representation learning"", ""clip""]",23,31d61d86-cac7-4d12-b305-367853800c62,2024-09-25,1.2388
iclr_SBCMNc3Mq3,2025,ECD: A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials,"Pin Chen, Zexin Xu, Qing Mo, Hongjin Zhong, Fengyang Xu, Yutong Lu",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[6, 6, 8, 6]",6.5,"[3, 3, 3, 2]",2.75,"[3, 3, 4, 2]",3.0,"[2, 3, 4, 2]",2.75,"[3, 4, 5, 4]",4.0,"[""electronic charge density"", ""crystalline inorganic materials"", ""graph neural network"", ""dataset""]",1,73c98dd0-d309-4136-babd-ff150feb1a03,2024-09-26,0.054
iclr_rwqShzb9li,2025,Linear Representations of Political Perspective Emerge in Large Language Models,"Junsol Kim, James Evans, Aaron Schein",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[6, 10, 8, 6]",7.5,"[3, 4, 3, 3]",3.25,"[3, 4, 4, 3]",3.5,"[3, 4, 3, 3]",3.25,"[4, 4, 4, 4]",4.0,"[""large language model"", ""political perspective"", ""ideology"", ""representation learning""]",53,c7b7f25e-6c5c-416f-aef2-2e7bfb9f44a3,2024-09-27,2.8649
iclr_rfdblE10qm,2025,Rethinking Reward Modeling in Preference-based Large Language Model Alignment,"Hao Sun, Yunyi Shen, Jean-Francois Ton","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 4, 3]",3.33,"[3, 4, 3]",3.33,"[3, 3, 3]",3.0,"[3, 4, 3]",3.33,"[""bradley-terry model"", ""reward modeling"", ""large language models""]",41,febd17c4-cc37-4880-a12e-27e2db808392,2024-09-26,2.2122
iclr_5U1rlpX68A,2025,SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning,"Yichen Wu, Hongming Piao, Long-Kai Huang, Renzhen Wang, Wanhua Li, Hanspeter Pfister, Deyu Meng, Kede Ma, Ying Wei","transfer learning, meta learning, and lifelong learning",Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 8]",7.5,"[3, 4, 4, 3]",3.5,"[3, 3, 3, 3]",3.0,"[3, 2, 4, 3]",3.0,"[5, 4, 5, 4]",4.5,"[""continual learning; low-rank adaptation""]",59,ef7f63bf-1686-4cf5-8d54-824ad55ebf93,2024-09-26,3.1835
iclr_vRvVVb0NAz,2025,When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers,"Hongkang Li, Yihua Zhang, Shuai Zhang, Pin-Yu Chen, Sijia Liu, Meng Wang",learning theory,Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 8]",7.5,"[3, 2, 3, 4]",3.0,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 4]",3.25,"[2, 3, 2, 3]",2.5,"[""task arithmetic"", ""generalization"", ""nonlinear transformers"", ""deep learning theory"", ""machine unlearning""]",37,708d06be-f67c-451f-b64c-9ffd507d01e3,2024-09-26,1.9964
iclr_or8mMhmyRV,2025,MaestroMotif: Skill Design from Artificial Intelligence Feedback,"Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani, Pascal Vincent, Amy Zhang, Pierre-Luc Bacon, Doina Precup, Marlos C. Machado, Pierluca D'Oro",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[5, 8, 10, 8]",7.75,"[2, 3, 4, 4]",3.25,"[2, 3, 3, 4]",3.0,"[2, 2, 4, 3]",2.75,"[2, 4, 4, 4]",3.5,"[""hierarchical rl"", ""reinforcement learning"", ""llms""]",14,62ff4adb-a522-4f63-8b54-57a87c023a81,2024-09-28,0.7581
iclr_xDrFWUmCne,2025,Learning to Discretize Denoising Diffusion ODEs,"Vinh Tong, Dung Trung Hoang, Anji Liu, Guy Van den Broeck, Mathias Niepert",generative models,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[2, 5, 4]",3.67,"[""diffusion models"", ""efficient sampling"", ""ordinary differentiable equations""]",27,cab30916-9740-4909-b704-de7c22b52944,2024-05-01,1.1555
iclr_7BLXhmWvwF,2025,Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects,"Tai Hoang, Huy Le, Philipp Becker, Vien Anh Ngo, Gerhard Neumann",reinforcement learning,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 4, 3, 3]",3.25,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 2]",2.75,"[3, 4, 3, 2]",3.0,"[""robotic manipulation"", ""equivariance"", ""graph neural networks"", ""reinforcement learning"", ""deformable objects""]",10,3add791c-58c9-4691-83c9-28dacf40e5e7,2024-09-25,0.5386
iclr_is4nCVkSFA,2025,Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model,"Siyu Chen, Beining Wu, Miao Lu, Zhuoran Yang, Tianhao Wang",learning theory,Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 4, 4, 3]",3.5,"[""single-index model"", ""feature learning"", ""gradient-based method"", ""computational-statistical tradeoff""]",6,9fa41c4c-9d97-4a30-a351-689b86ac9d27,2024-09-28,0.3249
iclr_VpWki1v2P8,2025,LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization,"Jui-Nan Yen, Si Si, Zhao Meng, Felix Yu, Sai Surya Duvvuri, Inderjit S Dhillon, Cho-Jui Hsieh, Sanjiv Kumar",optimization,Accept (Oral),ICLR 2025 Oral,"[10, 8, 8]",8.67,"[3, 3, 4]",3.33,"[4, 4, 4]",4.0,"[4, 3, 3]",3.33,"[3, 4, 3]",3.33,"[""optimization"", ""lora""]",4,9609b19a-cf58-49e7-8d6a-3cd5ddcda1a8,2024-09-27,0.2162
iclr_fAAaT826Vv,2025,BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models,"Yu Feng, Ben Zhou, Weidong Lin, Dan Roth","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 4, 3]",3.33,"[2, 3, 4]",3.0,"[3, 4, 3]",3.33,"[3, 3, 3]",3.0,"[""large language models"", ""reasoning"", ""planning"", ""trustworthiness"", ""interpretability"", ""probability estimation"", ""bayesian methods""]",33,2c4a8141-ca6a-4c58-a05c-69a7db6144cb,2024-04-01,1.3543
iclr_u1cQYxRI1H,2025,Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport,"Lvmin Zhang, Anyi Rao, Maneesh Agrawala","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[10, 10, 10, 10]",10.0,"[4, 3, 3, 4]",3.5,"[4, 4, 4, 3]",3.75,"[4, 4, 4, 4]",4.0,"[5, 5, 3, 4]",4.25,"[""diffusion model"", ""illumination editing"", ""image editing""]",116,56601aac-1504-444a-8f9e-8736b55a8ca3,2024-09-24,6.2366
iclr_CLE09ESvul,2025,What should a neuron aim for? Designing local objective functions based on information theory,"Andreas Christian Schneider, Valentin Neuhaus, David Alexander Ehrlich, Abdullah Makkeh, Alexander S Ecker, Viola Priesemann, Michael Wibral",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 6]",7.5,"[3, 4, 3, 2]",3.0,"[3, 4, 3, 2]",3.0,"[4, 3, 3, 3]",3.25,"[3, 5, 4, 3]",3.75,"[""local learning"", ""interpretability"", ""neuro-inspired"", ""information theory"", ""partial information decomposition""]",4,84a8e35a-389c-4473-85ff-9fb7e3a89b50,2024-09-27,0.2162
iclr_vf5aUZT0Fz,2025,DEPT: Decoupled Embeddings for Pre-training Language Models,"Alex Iacob, Lorenzo Sani, Meghdad Kurmanji, William F. Shen, Xinchi Qiu, Dongqi Cai, Yan Gao, Nicholas Donald Lane","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[2, 2, 3]",2.33,"[3, 4, 3]",3.33,"[3, 4, 4]",3.67,"[4, 5, 4]",4.33,"[""decentralized training"", ""federated learning"", ""multi-domain training"", ""multilingual training""]",3,c1d2f641-6b00-413c-b4a1-b4a160d91d73,2024-09-27,0.1622
iclr_4FWAwZtd2n,2025,Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning,"Charlie Victor Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 6, 8, 8]",7.5,"[3, 3, 3, 4]",3.25,"[3, 3, 4, 4]",3.5,"[4, 3, 4, 3]",3.5,"[4, 3, 5, 4]",4.0,"[""test-time compute"", ""llms"", ""scaling"", ""language models""]",254,35ffa3f5-882d-49e7-8f52-7fa40f6e05b0,2024-09-27,13.7297
iclr_FBkpCyujtS,2025,Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs,"Nguyen Nhat Minh, Andrew Baker, Clement Neo, Allen G Roush, Andreas Kirsch, Ravid Shwartz-Ziv","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 10, 10, 6]",8.5,"[3, 3, 4, 3]",3.25,"[2, 4, 4, 3]",3.25,"[2, 4, 4, 3]",3.25,"[4, 4, 4, 4]",4.0,"[""natural language processing"", ""large language models"", ""text generation"", ""sampling methods"", ""truncation sampling"", ""stochastic sampling"", ""min-p sampling"", ""top-p sampling"", ""nucleus sampling"", ""temperature sampling"", ""decoding methods"", ""deep learning"", ""artificial intelligence""]",107,73e4c058-9899-40bf-9251-dc68548ab334,2024-07-01,5.0078
iclr_QEHrmQPBdd,2025,RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style,"Yantao Liu, Zijun Yao, Rui Min, Yixin Cao, Lei Hou, Juanzi Li",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 3, 4]",3.33,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[4, 3, 4]",3.67,"[""reward models"", ""language models"", ""evaluation"", ""alignment""]",107,1c412f1d-ef8b-4cd1-acbc-973faa57ef6b,2024-09-28,5.7942
iclr_vo9t20wsmd,2025,Faster Cascades via Speculative Decoding,"Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Seungyeon Kim, Neha Gupta, Aditya Krishna Menon, Sanjiv Kumar",generative models,Accept (Oral),ICLR 2025 Oral,"[3, 6, 8]",5.67,"[3, 4, 3]",3.33,"[1, 3, 4]",2.67,"[1, 3, 3]",2.33,"[4, 2, 3]",3.0,"[""cascades"", ""speculative decoding"", ""speculative execution"", ""llm"", ""inference"", ""adaptive inference""]",30,94d4ea20-1dd9-4019-840e-3a47fdf23da5,2024-05-01,1.2839
iclr_pQqeQpMkE7,2025,On Scaling Up 3D Gaussian Splatting Training,"Hexu Zhao, Haoyang Weng, Daohan Lu, Ang Li, Jinyang Li, Aurojit Panda, Saining Xie","infrastructure, software libraries, hardware, systems, etc.",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 4, 3, 2]",3.25,"[3, 3, 3, 4]",3.25,"[4, 4, 3, 4]",3.75,"[5, 5, 4, 4]",4.5,"[""gaussian splatting"", ""machine learning system"", ""distributed training""]",47,cf530c7b-f4fa-4922-b69d-82f1a0e0fd3c,2024-09-26,2.536
iclr_ijbA5swmoK,2025,Second-Order Min-Max Optimization with Lazy Hessians,"Lesi Chen, Chengchang Liu, Jingzhao Zhang",optimization,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 6]",7.5,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 3]",2.75,"[4, 4, 3, 4]",3.75,"[""min-max optimization; second-order methods; computational complexity""]",8,e84acbe7-fabc-407f-ac63-0616166ab76c,2024-09-24,0.4301
iclr_pISLZG7ktL,2025,Data Scaling Laws in Imitation Learning for Robotic Manipulation,"Fanqi Lin, Yingdong Hu, Pingyue Sheng, Chuan Wen, Jiacheng You, Yang Gao","applications to robotics, autonomy, planning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 4, 4, 4]",4.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 4]",3.25,"[5, 3, 4, 4]",4.0,"[""data scaling laws"", ""imitation learning"", ""robotic manipulation""]",144,e83d4c0f-83a6-4051-9367-3c06da67f41a,2024-09-26,7.7698
iclr_NN6QHwgRrQ,2025,MAP: Multi-Human-Value Alignment Palette,"Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[4, 4, 4]",4.0,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[4, 4, 4]",4.0,"[""human value alignment"", ""generative model""]",19,3462bb50-298e-468c-895b-4ed0b68384ce,2024-09-28,1.0289
iclr_EzjsoomYEb,2025,Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity,"Yam Eitan, Yoav Gelberg, Guy Bar-Shalom, Fabrizio Frasca, Michael M. Bronstein, Haggai Maron",learning on graphs and other geometries & topologies,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[2, 2, 2]",2.0,"[3, 3, 3]",3.0,"[3, 4, 3]",3.33,"[4, 2, 3]",3.0,"[""topological deep learning"", ""message passing"", ""higher order message passing"", ""expressivity"", ""graph neural networks"", ""gnns"", ""topology"", ""homology"", ""symmetry""]",17,4e7d29c6-98c4-4db5-8a44-9ccd003a2e05,2024-08-01,0.8347
iclr_esYrEndGsr,2025,Influence Functions for Scalable Data Attribution in Diffusion Models,"Bruno Kacper Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[6, 8, 10]",8.0,"[2, 3, 3]",2.67,"[3, 3, 3]",3.0,"[3, 3, 4]",3.33,"[4, 2, 4]",3.33,"[""diffusion models"", ""influence functions"", ""generalised gauss newton"", ""ggn"", ""data attribution"", ""hessian approximation"", ""interpretability"", ""curvature"", ""kronecker-factored approximate curvature"", ""k-fac""]",26,73e8233e-1fd6-4b25-bed8-0f13d7394eb5,2024-09-24,1.3978
iclr_GRMfXcAAFh,2025,Oscillatory State-Space Models,"T. Konstantin Rusch, Daniela Rus",learning on time series and dynamical systems,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 3]",3.25,"[3, 4, 3, 3]",3.25,"[3, 4, 3, 2]",3.0,"[5, 2, 4, 3]",3.5,"[""state-space models"", ""sequence models"", ""oscillators"", ""long-range interactions"", ""time-series""]",28,4866f85f-77cc-4550-9583-98d7972eec01,2024-09-27,1.5135
iclr_8EfxjTCg2k,2025,MoDeGPT: Modular Decomposition for Large Language Model Compression,"Chi-Heng Lin, Shangqian Gao, James Seale Smith, Abhishek Patel, Shikhar Tuli, Yilin Shen, Hongxia Jin, Yen-Chang Hsu","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 3]",2.75,"[3, 3, 2, 3]",2.75,"[3, 2, 4, 3]",3.0,"[""llm"", ""model compression"", ""matrix decomposition""]",42,e57b0629-399a-46e1-8a20-5fcc649201d2,2024-08-01,2.0622
iclr_n2NidsYDop,2025,Transformers Provably Solve Parity Efficiently with Chain of Thought,"Juno Kim, Taiji Suzuki",learning theory,Accept (Oral),ICLR 2025 Oral,"[10, 8, 8]",8.67,"[4, 3, 4]",3.67,"[4, 3, 4]",3.67,"[4, 3, 3]",3.33,"[3, 3, 4]",3.33,"[""transformers"", ""chain of thought"", ""parity"", ""self-consistency""]",56,6047fd83-4a2b-4446-97c4-df4ba37e6de0,2024-09-27,3.027
iclr_BPgK5XW1Nb,2025,Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment,"Dongyoung Kim, Kimin Lee, Jinwoo Shin, Jaehyung Kim",generative models,Accept (Oral),ICLR 2025 Oral,"[10, 8, 8]",8.67,"[4, 3, 4]",3.67,"[4, 3, 4]",3.67,"[3, 3, 3]",3.0,"[4, 4, 3]",3.67,"[""large language model"", ""alignment"", ""preference""]",23,341a37c4-f9e2-43c4-b6cc-71994ae6811f,2024-06-01,1.0283
iclr_YLIsIzC74j,2025,LaMPlace: Learning to Optimize Cross-Stage Metrics in Macro Placement,"Zijie Geng, Jie Wang, Ziyan Liu, Siyuan Xu, Zhentao Tang, Shixiong Kai, Mingxuan Yuan, Jianye HAO, Feng Wu","applications to robotics, autonomy, planning",Accept (Oral),ICLR 2025 Oral,"[6, 8, 8, 8]",7.5,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 3]",3.25,"[3, 4, 3, 3]",3.25,"[5, 4, 4, 4]",4.25,"[""macro placement"", ""chip design"", ""eda""]",10,5f1c1fa0-2fec-4165-8590-b0da987c7501,2024-09-26,0.5396
iclr_kxnoqaisCT,2025,Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents,"Boyu Gou, Ruohan Wang, Boyuan Zheng, Yanan Xie, Cheng Chang, Yiheng Shu, Huan Sun, Yu Su","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 8, 5, 10]",7.75,"[3, 4, 3, 4]",3.5,"[3, 3, 2, 3]",2.75,"[3, 3, 2, 3]",2.75,"[4, 5, 4, 5]",4.5,"[""gui agents"", ""visual grounding"", ""multimodal large language models"", ""gui grounding"", ""large language model""]",280,aa40b9ff-47ad-42b4-b3da-8fdb5fc8b079,2024-09-26,15.1079
iclr_f4gF6AIHRy,2025,Combatting Dimensional Collapse in LLM Pre-Training Data via Submodular File Selection,"Ziqing Fan, Siyuan Du, Shengchao Hu, Pingjie Wang, Li Shen, Ya Zhang, Dacheng Tao, Yanfeng Wang","other topics in machine learning (i.e., none of the above)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 3, 4, 2]",3.0,"[3, 3, 3, 2]",2.75,"[3, 2, 4, 2]",2.75,"[3, 4, 4, 2]",3.25,"[""file selection"", ""large language model"", ""pre-training"", ""submodular optimization""]",7,ce966705-a35e-44ac-b911-a8da91d4411a,2024-09-26,0.3777
iclr_pqOjj90Vwp,2025,Towards a Complete Logical Framework for GNN Expressiveness,Tuo Xu,learning on graphs and other geometries & topologies,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[2, 2, 3]",2.33,"[3, 4, 3]",3.33,"[2, 4, 4]",3.33,"[3, 2, 3]",2.67,"[""graph neural networks"", ""logic""]",2,8b5d5227-3fe2-4591-aa12-70d43513817c,2024-09-26,0.1079
iclr_P7KIGdgW8S,2025,On the Hölder Stability of Multiset and Graph Neural Networks,"Yair Davidson, Nadav Dym",learning on graphs and other geometries & topologies,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 2, 3, 3]",2.75,"[3, 4, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 4, 3]",3.25,"[""graph neural networks"", ""message passing neural networks"", ""multiset neural networks"", ""neural network stability"", ""expressive power"", ""wl tests""]",10,2766ec7a-49eb-4cf7-9d8f-d25d88d19762,2024-06-01,0.4471
iclr_TVQLu34bdw,2025,Proteina: Scaling Flow-based Protein Structure Generative Models,"Tomas Geffner, Kieran Didi, Zuobai Zhang, Danny Reidenbach, Zhonglin Cao, Jason Yim, Mario Geiger, Christian Dallago, Emine Kucukbenli, Arash Vahdat, Karsten Kreis","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 6, 5, 6, 8]",6.6,"[4, 3, 4, 4, 4]",3.8,"[4, 3, 4, 3, 4]",3.6,"[4, 3, 3, 2, 4]",3.2,"[3, 4, 3, 5, 4]",3.8,"[""protein structure generation"", ""de novo protein design"", ""flow matching"", ""fold class conditioning""]",82,47045b07-b3d1-497f-bd0e-135df0d260de,2024-09-27,4.4324
iclr_mMPMHWOdOy,2025,WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct,"Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Jian-Guang Lou, Chongyang Tao, Xiubo Geng, Qingwei Lin, Shifeng Chen, Yansong Tang, Dongmei Zhang","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[2, 3, 3, 3]",2.75,"[3, 4, 4, 3]",3.5,"[4, 3, 4, 4]",3.75,"[3, 3, 3, 4]",3.25,"[""mathematical reasoning"", ""evol-instruct"", ""reinforcement learning""]",724,8f5b9147-8405-4169-803b-a45af9daca17,2023-08-01,22.3687
iclr_ja4rpheN2n,2025,GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation,"Ziwei Yang, Zheng Chen, Xin Liu, Rikuto Kotoge, Peng Chen, Yasuko Matsubara, Yasushi Sakurai, Jimeng Sun",learning on graphs and other geometries & topologies,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[4, 3, 4]",3.67,"[4, 2, 4]",3.33,"[4, 3, 3]",3.33,"[4, 3, 4]",3.67,"[""gene functional networks"", ""disease subtypes"", ""bioinformatics""]",5,69348f16-8594-4329-86c5-96f245140cda,2024-09-27,0.2703
iclr_kGvXIlIVLM,2025,Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment,"Huayu Chen, Hang Su, Peize Sun, Jun Zhu",generative models,Accept (Oral),ICLR 2025 Oral,"[6, 8, 6, 6, 8, 8]",7.0,"[3, 2, 3, 3, 4, 3]",3.0,"[3, 3, 4, 3, 3, 3]",3.17,"[3, 3, 3, 3, 4, 3]",3.17,"[3, 3, 4, 4, 4, 3]",3.5,"[""autoregressive"", ""generative models"", ""image generation"", ""multimodal"", ""alignment"", ""rlhf"", ""classifier-free guidance""]",13,aae922ef-8689-4650-bc9a-6383117a9941,2024-09-27,0.7027
iclr_hrqNOxpItr,2025,Cross-Entropy Is All You Need To Invert the Data Generating Process,"Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E Vogt, Randall Balestriero, Wieland Brendel, David Klindt","unsupervised, self-supervised, semi-supervised, and supervised representation learning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 4, 2]",3.0,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[4, 4, 4]",4.0,"[""supervised learning"", ""representation learning"", ""identifiability"", ""linear representation hypothesis""]",22,1e90d2fa-4cf7-467b-a9ad-74f007007084,2024-09-27,1.1892
iclr_UHPnqSTBPO,2025,Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement,"Jaehun Jung, Faeze Brahman, Yejin Choi","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[10, 8, 8, 6]",8.0,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[4, 3, 3, 3]",3.25,"[5, 3, 5, 3]",4.0,"[""large language model"", ""llm"", ""llm judge"", ""evaluation"", ""alignment""]",58,655559ef-1584-4574-9b70-1881ace272ed,2024-07-01,2.7145
iclr_je3GZissZc,2025,Instant Policy: In-Context Imitation Learning via Graph Diffusion,"Vitalis Vosylius, Edward Johns","applications to robotics, autonomy, planning",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 3]",3.25,"[4, 3, 2, 3]",3.0,"[3, 3, 2, 3]",2.75,"[4, 3, 4, 3]",3.5,"[""in-context imitation learning"", ""robotic manipulation"", ""graph neural networks"", ""diffusion models""]",35,43cbfe0c-8084-4882-a031-df27101013ac,2024-09-27,1.8919
iclr_E4Fk3YuG56,2025,Cut Your Losses in Large-Vocabulary Language Models,"Erik Wijmans, Brody Huval, Alexander Hertzberg, Vladlen Koltun, Philipp Kraehenbuehl","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[10, 6, 8, 10]",8.5,"[3, 2, 3, 3]",2.75,"[4, 3, 3, 3]",3.25,"[4, 3, 3, 4]",3.5,"[4, 2, 4, 3]",3.25,"[""large language model"", ""large vocabulary"", ""efficient""]",24,acb1988b-f82e-4136-9b85-ba2db3664a54,2024-09-17,1.2743
iclr_xyfb9HHvMe,2025,DSPO: Direct Score Preference Optimization for Diffusion Model Alignment,"Huaisheng Zhu, Teng Xiao, Vasant G Honavar","applications to computer vision, audio, language, and other modalities",Accept (Oral),ICLR 2025 Oral,"[8, 6, 6]",6.67,"[3, 3, 2]",2.67,"[4, 3, 3]",3.33,"[4, 3, 3]",3.33,"[2, 4, 5]",3.67,"[""text-to-image generation""]",41,205e99e0-be14-41a4-9c4b-e9702f87c29b,2024-09-26,2.2122
iclr_t7P5BUKcYv,2025,MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts,"Peng Jin, Bo Zhu, Li Yuan, Shuicheng YAN","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 3, 4]",3.33,"[2, 3, 3]",2.67,"[2, 3, 3]",2.67,"[4, 3, 5]",4.0,"[""mixture of experts"", ""large language models"", ""efficient foundation models""]",41,15e9bd9d-78a6-45f2-8285-5ebcdfbcb212,2024-09-25,2.2083
iclr_tTPHgb0EtV,2025,Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation,"Tiansheng Huang, Sihao Hu, Fatih Ilhan, Selim Furkan Tekin, Ling Liu","alignment, fairness, safety, privacy, and societal considerations",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 4]",3.5,"[""harmful fine-tuning"", ""llm"", ""safety alignment""]",64,4d8b1052-3cc0-43b7-86ea-58be1fa8b4e2,2024-09-01,3.3046
iclr_9VGTk2NYjF,2025,The Complexity of Two-Team Polymatrix Games with Independent Adversaries,"Alexandros Hollender, Gilbert Maystre, Sai Ganesh Nagarajan",optimization,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8]",8.0,"[3, 4, 4]",3.67,"[4, 4, 4]",4.0,"[3, 3, 3]",3.0,"[4, 3, 3]",3.33,"[""algorithmic game theory"", ""nash equilibrium"", ""minmax optimization""]",6,b6fd2e53-6a35-4353-af7a-ed0a22969914,2024-09-01,0.3098
iclr_LyJi5ugyJx,2025,"Simplifying, Stabilizing and Scaling Continuous-time Consistency Models","Cheng Lu, Yang Song",generative models,Accept (Oral),ICLR 2025 Oral,"[10, 10, 8, 10, 8]",9.2,"[3, 4, 4, 3, 3]",3.4,"[4, 4, 3, 3, 3]",3.4,"[4, 4, 3, 4, 3]",3.6,"[5, 4, 3, 4, 4]",4.0,"[""continuous-time consistency models"", ""diffusion models"", ""fast sampling""]",198,7de06388-66bc-4d47-add3-a044dc96726e,2024-09-19,10.5506
iclr_YUYJsHOf3c,2025,ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement,"XIANGYU PENG, Congying Xia, Xinyi Yang, Caiming Xiong, Chien-Sheng Wu, Chen Xing","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[4, 4, 5, 4]",4.25,"[""llm"", ""reasoning"", ""generalization"", ""self-improvement""]",20,efe80f89-bb8f-423f-becc-f5e9c537ee0f,2024-09-27,1.0811
iclr_kbjJ9ZOakb,2025,Learning and aligning single-neuron invariance manifolds in visual cortex,"Mohammad Bashiri, Luca Baroni, Ján Antolík, Fabian H. Sinz",applications to neuroscience & cognitive science,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[3, 4, 3, 4]",3.5,"[3, 3, 3, 4]",3.25,"[4, 3, 3, 3]",3.25,"[5, 5, 2, 5]",4.25,"[""neural invariances"", ""invariance manifold"", ""mei"", ""implicit neural representations"", ""contrastive learning"", ""invariance alignment"", ""clustering"", ""visual cortex"", ""macaque v1"", ""primary visual cortex""]",4,35f19981-dda9-4b22-9681-cea3b079bed8,2024-09-26,0.2158
iclr_6s5uXNWGIh,2025,MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering,"Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Aleksander Madry, Lilian Weng",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 10, 6]",8.0,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 3]",3.25,"[3, 4, 4, 4]",3.75,"[""benchmark"", ""evals"", ""evaluations"", ""dataset"", ""tasks"", ""data science"", ""engineering"", ""agents"", ""language agents"", ""scaffold"", ""coding"", ""swe"", ""mle""]",284,32112b5c-6801-47bb-bb99-63805fee25d9,2024-09-26,15.3237
iclr_kX8h23UG6v,2025,Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization,"Zhitong Xu, Haitao Wang, Jeff M. Phillips, Shandian Zhe","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8, 8]",7.6,"[3, 4, 2, 2, 3]",2.8,"[4, 3, 2, 3, 3]",3.0,"[3, 4, 2, 3, 3]",3.0,"[3, 4, 3, 4, 5]",3.8,"[""gaussian process"", ""bayesian optimization"", ""high dimensional bayesian optimization""]",41,cec313d1-d755-4ca2-be8d-02c323b306bf,2024-02-01,1.555
iclr_QWunLKbBGF,2025,Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs,"Siyan Zhao, Mingyi Hong, Yang Liu, Devamanyu Hazarika, Kaixiang Lin",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[6, 8, 8, 8]",7.5,"[3, 3, 4, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 4]",3.25,"[4, 3, 3, 4]",3.5,"[""personalization"", ""benchmark"", ""large language models"", ""conversational llm"", ""chatbots""]",84,674a3fb1-d7a0-4b29-b936-9999db38b57c,2024-09-27,4.5405
iclr_eBS3dQQ8GV,2025,Emergence of meta-stable clustering in mean-field transformer models,"Giuseppe Bruno, Federico Pasqualotto, Andrea Agazzi","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 10, 5, 8]",7.8,"[3, 3, 4, 3, 4]",3.4,"[3, 3, 4, 3, 4]",3.4,"[3, 3, 4, 2, 2]",2.8,"[3, 2, 3, 1, 2]",2.2,"[""mean-field limits"", ""transformers"", ""meta-stability"", ""clustering""]",30,95b49a3b-12ae-4966-ba70-7e79ad1ad4fc,2024-09-26,1.6187
iclr_SPS6HzVzyt,2025,Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance,"Sachin Goyal, Christina Baek, J Zico Kolter, Aditi Raghunathan","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 3, 4]",3.5,"[3, 3, 2, 4]",3.0,"[3, 3, 2, 4]",3.0,"[4, 3, 4, 4]",3.75,"[""instruction finetuning"", ""context-vs-parametric reliance""]",11,dd5e1e1a-7219-489f-97e0-dcfd8f3d4f75,2024-09-27,0.5946
iclr_QKBu1BOAwd,2025,From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions,"Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, Ji-Rong Wen","other topics in machine learning (i.e., none of the above)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6]",7.33,"[4, 3, 3]",3.33,"[3, 4, 2]",3.0,"[4, 4, 2]",3.33,"[4, 4, 3]",3.67,"[""large language model"", ""tool learning"", ""learning from experience""]",46,b871c6da-045e-48df-9d50-00d5872f7a6f,2024-09-27,2.4865
iclr_EO8xpnW7aX,2025,SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups,"Yongxing Zhang, Donglin Yang, Renjie Liao",generative models,Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8, 8]",8.0,"[3, 4, 4, 3, 3]",3.4,"[3, 4, 4, 3, 3]",3.4,"[3, 3, 4, 3, 3]",3.2,"[3, 3, 2, 4, 3]",3.0,"[""finite symmetric groups"", ""discrete diffusion"", ""permutations"", ""riffle shuffles"", ""plackett-luce distribution"", ""sorting"", ""jigsaw puzzle""]",4,b4aa1805-50f6-4a21-b1b1-7ee675186f2e,2024-09-26,0.2158
iclr_WCRQFlji2q,2025,Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models,"Javier Ferrando, Oscar Balcells Obeso, Senthooran Rajamanoharan, Neel Nanda",interpretability and explainable AI,Accept (Oral),ICLR 2025 Oral,"[10, 8, 10, 8]",9.0,"[4, 4, 4, 3]",3.75,"[4, 4, 3, 3]",3.5,"[3, 3, 4, 3]",3.25,"[4, 4, 5, 4]",4.25,"[""mechanistic interpretability"", ""hallucinations"", ""language models""]",95,e68a18dc-519e-44ec-abaa-96852e7325f8,2024-09-27,5.1351
iclr_jOmk0uS1hl,2025,Training on the Test Task Confounds Evaluation and Emergence,"Ricardo Dominguez-Olmedo, Florian E. Dorner, Moritz Hardt","foundation or frontier models, including LLMs",Accept (Oral),ICLR 2025 Oral,"[8, 8, 8, 8]",8.0,"[4, 3, 4, 4]",3.75,"[3, 3, 3, 3]",3.0,"[4, 3, 3, 3]",3.25,"[3, 4, 4, 4]",3.75,"[""language models"", ""benchmarking"", ""emergence""]",36,35c1a11f-f39b-4655-ac65-3121dfc89b8b,2024-07-01,1.6849
iclr_o9kqa5K3tB,2025,On the Benefits of Memory for Modeling Time-Dependent PDEs,"Ricardo Buitrago, Tanya Marwah, Albert Gu, Andrej Risteski","applications to physical sciences (physics, chemistry, biology, etc.)",Accept (Oral),ICLR 2025 Oral,"[8, 8, 6, 8]",7.5,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 3]",2.75,"[3, 3, 3, 3]",3.0,"[3, 4, 4, 3]",3.5,"[""state space models"", ""partial differential equations""]",11,c9a39b60-703c-47cb-ad1f-0591ef42961d,2024-09-01,0.568
iclr_tc90LV0yRL,2025,Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models,"Andy K Zhang, Neil Perry, Riya Dulepet, Joey Ji, Celeste Menders, Justin W Lin, Eliot Jones, Gashon Hussein, Samantha Liu, Donovan Julian Jasper, Pura Peetathawatchai, Ari Glenn, Vikram Sivashankar, Daniel Zamoshchin, Leo Glikbarg, Derek Askaryar, Haoxiang Yang, Aolin Zhang, Rishi Alluri, Nathan Tran, Rinnara Sangpisit, Kenny O Oseleononmen, Dan Boneh, Daniel E. Ho, Percy Liang",datasets and benchmarks,Accept (Oral),ICLR 2025 Oral,"[8, 8, 10]",8.67,"[3, 4, 3]",3.33,"[3, 3, 4]",3.33,"[3, 4, 4]",3.67,"[4, 3, 4]",3.67,"[""language model agents"", ""benchmark"", ""cybersecurity"", ""risk""]",142,3175e964-be51-4b37-b377-8ae9c5dd0631,2024-08-01,6.9722